{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "device(type='cuda')"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from utils import *\n",
    "import os\n",
    "import pandas as pd\n",
    "import torch\n",
    "torch.cuda.is_available()\n",
    "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n",
    "os.environ['CUDA_LAUNCH_BLOCKING'] = '1'\n",
    "device"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 加载数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.read_csv('./processed_data/non_proc_1-9.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "split_index = int(len(data) * 0)\n",
    "data = data.iloc[split_index:].reset_index(drop=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "data['数据采集时间'] = change_df_sec(data['数据采集时间'])\n",
    "X = data.drop(['最高报警等级'], axis=1)\n",
    "\n",
    "y = data['最高报警等级']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## LSTMCNN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LSTMCNN(\n",
       "  (lstm): LSTM(20, 64, num_layers=2, batch_first=True)\n",
       "  (conv1): Conv1d(64, 64, kernel_size=(3,), stride=(1,), padding=(1,))\n",
       "  (pool): MaxPool1d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
       "  (conv2): Conv1d(64, 128, kernel_size=(3,), stride=(1,), padding=(1,))\n",
       "  (fc1): Linear(in_features=384, out_features=100, bias=True)\n",
       "  (dropout): Dropout(p=0.5, inplace=False)\n",
       "  (fc2): Linear(in_features=100, out_features=4, bias=True)\n",
       ")"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from model import LSTMCNN\n",
    "lstm_model = torch.load('./model/lstm_cnn_last.pth')\n",
    "lstm_model.to(device)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Testing: 100%|██████████| 1852/1852 [00:07<00:00, 237.36it/s, accuracy=70.96%]\n",
      "d:\\Python\\CondaEnvs\\Pytorch\\lib\\site-packages\\sklearn\\metrics\\_classification.py:1565: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n",
      "d:\\Python\\CondaEnvs\\Pytorch\\lib\\site-packages\\sklearn\\metrics\\_classification.py:1565: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n",
      "d:\\Python\\CondaEnvs\\Pytorch\\lib\\site-packages\\sklearn\\metrics\\_classification.py:1565: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Confusion Matrix:\n",
      "[[162933   4182  58249      0]\n",
      " [     1      0      0      0]\n",
      " [  5092    182   2570      0]\n",
      " [    30      0      0      0]]\n",
      "\n",
      "Classification Report:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.97      0.72      0.83    225364\n",
      "           1       0.00      0.00      0.00         1\n",
      "           2       0.04      0.33      0.07      7844\n",
      "           3       0.00      0.00      0.00        30\n",
      "\n",
      "    accuracy                           0.71    233239\n",
      "   macro avg       0.25      0.26      0.23    233239\n",
      "weighted avg       0.94      0.71      0.80    233239\n",
      "\n"
     ]
    },
    {
     "data": {
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rYG6u/lLwRERERERlBQvjRERERERUPpWltdSJiEhjL5NfovOBzirxO0PvqF0U//NZPHr99LtK/OdvvoRLLUuNc8yL1NRUzJw5E35+fmLMzMwM69evx4ABAwr13kREREREpQEL40REREREREREVC5FJUTB47CHSlzdmeIKhYCa353M9tyd7zvB1FBH4xzzKiEhAT///LN43KFDB2zbtg3W1taFfm8iIiIiotJAq7gTICIiIiIiIiIiKmp7H+5VKYpXN6qOe1731CqK77r25JNF8Yfz3YukKA4AVatWxcaNG6Gvr481a9YgMDCQRXEiIiIion/gjHEiIiIiIiqXJFxLnYioXIpNi812P/ExjcZgfOPxavV1LSIOs4/cV4nvH+2M5vYWGueYF+Hh4ahYsSIsLP6+T58+feDs7Ixq1aoV6r2JiIiIiEojzhgnIiIiIiIiIqISJ/H0acjfvCnQPo9GHM22KD6z+Uy1i+IAMHDTdaVjxxpmiFrctVCL4oIgYP369WjcuDHGjRuncp5FcSIiIiKi7HHGOBERERERlUtqrJJLRETFIGaNn/h3rQoV8tXXp2aJ2xjb4NBXh6CnradWfwqFgHrfn1aK7RzRHK0/s8pXnrl59eoVvL29cerUKQDAL7/8gn79+qFPnz6Fel8iIiIiorKAhXEiIiIiIiIiIipxFCkp4t+tJk3SuJ+H7x6i37F+KvFFrovQo1YPtfvbeDECi089VIkXdlH8wIEDGD16NOLi4sTYmDFj4O7uXqj3JSIiIiIqK1gYJyIiIiIiIiKiEktauTJM3Dur3S5TkYkmu5pke+6S5yWY65ur3eeGixHwzaYofnVmO7X7yquEhARMnDgRu3btEmNVqlTBli1b0LVr10K7LxERERFRWcPCOBERERERlUtcSZ2IqOzKyMpA091NVeJL2yyFu51mM6ztZp5QiXVrWBVrBzpCUkj7c1y4cAFeXl549uyZGOvTpw82bNgAS0vLQrknEREREVFZxcI4ERERERERERGVGbIsGZrtbqYS3999P+pVrKd2f49eJ6Hzqksq8eMTXdGguqlGOebFzZs30a5dOwiCAAAwMTHB2rVrMWTIkEIrxBMRERERlWUsjBMRERERUfnEmgIRUZmjEBQqRXFbE1sc73Vco/6+3nIDl8NjVeLBPh1gaaSnUZ955eTkhB49euDo0aNo27Yttm/fDltb20K9JxERERFRWcbCOBERERERERERlXqCIKDRzkZKsV61e+GHlj9o1F/PdVcR8vy9UkyqJcHjRYWzr7cgCEozwSUSCTZt2oT27dtjwoQJ0NLSKpT7EhERERGVF/yNmoiIiIiIiIiISrWtoVvRcGdDpVgX+y4aFcUFQYDdzBMqRXG/gY6FVhSPjIxE69atceKE8j7mlSpVwqRJk1gUJyIiIiIqAJwxTkRERERE5ZKEa6kTEZUJfY/2xaP4R0qxehb18GPrHzXqz37WSZXY5RlusLEw1Ki/nAiCgK1bt2LKlClITk6Gt7c3QkNDYWlpWeD3IiIiIiIq7/h1UyIiIiIiIiIiKpVW3VqlUhTvU6cP9vfYr3ZfUbEpsJt5QiX+cL57oRTF3759i549e2LkyJFITk4GABgaGuLly5cFfi8iIiIiIuKMcSIiIiIiIiIiKoV6HOqBJ4lPlGIhX4dAW0tbrX7SM7MwetctXPwrRuVc1OKuSvt+F5SjR49i5MiRiIn5+57e3t5YuXIljI2NC/x+RERERETEwjgREREREZVThVDnICKiIrLw+kKVonjwkGC1i+IA8Pns09nGn/h20yS1HCUlJWHq1KnYsmWLGLOyssLmzZvh4eFR4PcjIiIiIqK/sTBORERERERERESlQpYiC90PdceL5BdK8ZO9T0JPW0+tvv58Fo9eP/2uEl/WrxH6NrXOV57ZuX37Nvr27YuoqCgx5uHhgU2bNqFSpUoFfj8iIiIiIlLGwjgREREREZVLnDBORFS6CIKAprubIkvIUorfHnIbOto6avX1++NYDNp8QyVeWEunA0DFihURFxcHADAyMsLq1asxfPjwQrsfEREREREp0yruBIiIiIiIiIiIiHLyMvklGu5sqFIUD+ofpHZRHEC2RfHHC7sUapHa1tYWa9euRcuWLXHnzh2MGDGCRXEiIiIioiLEwjgREREREREREZVYafI0dD7QWSlmomuCe173YGlgqXZ/djNPKB3P86iPJ77dINUuuI/JFAoFNmzYgMTERKX4kCFDcPHiRdSsWbPA7kVERERERHnDwjgREREREZVPkhLwIiKiTxIEAQCQmKFcXG5foz2uDLiiUZ+nQ1+pxLxc7DTq61OePXuGDh06YOzYsZg6darSOYlEAm1t7QK9HxERERER5Q0L40RERERERETlUGhoKJycnGBubo7p06eLRcic+Pv7o2rVqtDR0UGnTp3w6pVqkZGoILxJeYO3aW9V4hs6bMAqt1UaLUG+P/g5xuy+rRSLWNRV4xz/TRAE7Nq1Cw4ODggKCgIAbNu2Dffv3y+wexARERERkeZYGCciIiIionJJUgL+EBUXmbhN0tMAAQAASURBVEyGHj16oGnTpggODkZYWBi2b9+eY5srV65g9uzZ2LVrF6KiopCeno5vv/22aBKmciU1MxUdAjooxSobVsaNQTfQsnpLjfp8/i4VMwLuKsW2DXOCtlbB/Lc4Li4O/fv3x9ChQ8Xl021sbHDu3DnUr1+/QO5BRERERET5w8I4ERERERERUTlz6tQpJCQkYMWKFahVqxYWLVqELVu25Njm0aNHWL9+PTp06ABra2sMHz4cwcHBRZQxlRdyhRxf/vylUkxfWx+/9fsNhjqGGvfb6scgpeMdI5rD7fNKGvf3T6dOnUKDBg0QEBAgxoYOHYp79+7Bzc2tQO5BRERERET5Jy3uBIiIiIiIiIioaN25cwctWrSAoeGHQmPDhg0RFhaWYxtvb2+l40ePHqF27dqFliOVP3KFHI67HFXipnqm+erXbuYJpeOJ7WqjzWdW+eoTAFJSUvDtt99iw4YNYszCwgIbN25E3759890/EREREREVLBbGiYiIiIioXNJge1qiMiMxMRH29vbisUQigba2NuLj42Fubp5r+7i4OGzcuBG7d+/+5DUymQwymUzpnkT/lHj6NGLW+EGRkgK5Qo649Dis/8d5ixQJACFf9/h3URwA/tOpbr76/Gjfvn1KRfEuXbpgy5YtqFq1aoH0T0REREREBYtLqRMRERERERGVM1KpFHp6ekoxfX19pKam5qn9uHHj4OLigm7dun3ymsWLF8PU1FR82djY5CtnKnti1vghIzIS8jdvgJg4VEyC0kui+FAU16pQQaP+f/njmUosfGGXfOX8T8OHD0eHDh1gaGiI9evX48SJEyyKExERERGVYJwxTkRERERERFTOWFhYIDQ0VCmWlJQEXV3dXNtu3boVly5dQkhISI7XzZo1C9OmTROPExMTWRwnJYqUlA//KwHijf6OV9CpACOdDwGtChVgNWmS2n2fDn2N/x64pxSLXNQVWlqaLxcSFxeHihUrisdaWlrYvn07UlNTUadOHY37JSIiIiKiosHCOBERERERlUtcSZ3KMycnJ2zevFk8fvLkCWQyGSwsLHJsd/PmTUyZMgXHjh1D5cqVc7xWT09PZVY60T+lylOhiw9F8bETPnxEtbTNUrjaueer39vP4jFm9y2lWLBPB42L4gqFAj/99BP++9//4vjx43BzcxPPVa9ePV+5EhERERFR0eFS6kRERERERETlTOvWrZGQkICdO3cCAHx9fdGhQwdoa2sjMTERmZmZKm3evHmDHj164L///S+aNm2K5ORkJCcnF3XqVAbIsmToc7QPkjKSlOJ7uu6Bez6K4oIgoOe6q+j90+9K8S1ezWBppNmXNKKjo+Hu7o6JEyciNTUVXl5eSEhI0DhHIiIiIiIqPiyMExERERFR+SQpAS+iYiKVSuHv748xY8agcuXKCAgIgK+vLwCgYcOGOHHihEqbvXv34u3bt/Dx8YGxsbH4IlLHlegraLa7Gf6K/0s5PuAKGlo1zFff9rNOIuT5e6WYbUVDtK+X8+oGn/LLL7/AwcEBZ8+eFWNfffUVdHR08pMmEREREREVEy6lTkRERERERFQO9ezZE+Hh4QgODoaLiwusrKwAfFhWPTtTpkzBlClTii5BKlMEQUDDndkXvq0MrGCqZ5qv/qf9EqISO/+fNqhpZaR6cS7i4+MxYcIE/Pzzz2KsWrVq2LZtGzp16pSfNImIiIiIqBixME5ERERERERUTlWvXp17JFOR6HRAtaCsq6ULIANaEs0XNEyRydHD7woiY1OU4o8XdoFUW/1+f/vtNwwbNgzR0dFizNPTEz/99BMsLCw0zpOIiIiIiIofl1InIiIiIqJySVIC/hARlXXPk57DYYcDXqe8Voof8jgEc33zfPX98HUi6s8JVCmK3/yuvUZF8bVr16Jjx45iUdzMzAw///wz9u3bx6I4EREREVEZwBnjRERERERERERU4AafHIy7MXdV4neH3oVEIkF4Pvp+8CoRXVZfVokfGOuCSib6GvXZqVMnGBgYIC0tDe3bt8f27dthbW2djyyJiIiIiKgkYWGciIiIiIjKJQknbBMRFYp0eTr6HuuLp4lPleJ1zOvgoMfBfPdvN/OESqxFTQvsG+Wcr34/++wzrFmzBqmpqZgwYQK0tLjQIhERERFRWcLCOBERERERERERFYgLzy9g4vmJKvHt7tvRtHLTfPWdlJ4Jh7lnVOKGutpqF8UfP36MefPmYePGjTA0NBTjI0eOzFeORERERERUcrEwTkRERERERERE+Xb91fVsi+Ifl07Pr+yK4r+OcYaTXd73/xYEAf7+/pg2bRpSU1Nhbm6ONWvW5Ds3IiIiIiIq+VgYJyIiIiKicokrqRMRFYw3KW/Q51gfJMgSlOJf1foK81vOL5Ci+ED/6yqxJ77d1Orj1atXGDlyJE6ePCnGTp8+jeTkZBgZGeU7RyIiIiIiKtlYGCciIiIiIiIiIo3cfnMbXqe9VOJHeh5BTdOaBXKPRvPOICEtUymmblH8wIEDGD16NOLi4sTY6NGjsWzZMhbFiYiIiIjKCRbGiYiIiIiIiIhIbc8Tn2dbFL/seRlm+mb57v9pXAraLL2gEr8/r3Oe+0hISMCkSZOwc+dOMVa5cmVs2bIF3bqpV1wnIiIiIqLSjYVxIiIiIiIqn7iWOhGRRjIVmfAO9Mafb/9Uik9tOhUjGowokHu8SUzPtigeOKU1Kujl7eOsCxcuwMvLC8+ePRNjvXr1wsaNG2FlZVUgeRIRERERUenBwjgREREREREREeVJxPsI9DzSUyW+ocMGtKzeskDu8SYxHV8uOqcSvzu3E0z0dfLcz7Vr18SiuLGxMfz8/DB06NAC2fOciIiIiIhKHxbGiYiIiIioXJJwyjgRkVpeJr/Mtih+tOdR2Jva57v/8DdJ6Ljykkp8dOuamNW1ntr9zZgxA8ePH4dUKsWOHTtgZ2eX7xyJiIiIiKj0YmGciIiIiIiIiIhy1fmA8t7eXe27YknrJfnuVxAE2M08ke05M0OdPBXFs7KycO3aNbi6uooxbW1tHD16FGZmZtDW1s53nkREREREVLppFXcCRERERERERERUsm28s1HpeFmbZQVSFAeAN4mybONTO3yGkO875do+KioKbdu2Rdu2bXH9+nWlcxUrVmRRnIiIiIiIAJTAGeMPHz7EkiVLEB8fjz59+uDrr7+Gn58ftm3bBm1tbfTv3x/Tp08v7jSJiIiIiKiU4xazRER5c/PVTawNWSseG0oN0dmucw4tspd4+jRi1vhBkZICAMh8G5PtphbbhjvBrW6lXPsTBAHbtm3D5MmTkZycDAAYNmwYQkNDIZWWuI+8iIiIiIiomJWodwkymQzt27dHly5d0L59eyxfvhx79+7Fw4cP8cMPP0BbWxs//vgjEhISsGDBguJOl4iIiIiIiIioTItNi4X3GW+l2Ll+5zTqK2aNHzIiI8Xjj0XxVKkeAMDb1R6zu3+Rp77evn2LUaNG4ciRI2LMzs4OmzZtYlGciIiIiIiyVaLeKYSEhMDc3BybN28GAHh6esLW1hZHjhxBp04fls5q0qQJWrduzcI4ERERERHlCyeMExHl7ETkCcy8PFMpdmXAFRjpGqndV3pmFl6+jIMlgCxIEK9vAuBDUXxXvc74eeSXcKltmae+jh49im+++QZv374VY97e3li5ciWMjY3Vzo2IiIiIiMqHElUYr1SpEmJjY5GWlgYDAwNUqlQJfn5+cHNzE6959eoV94YiIiIiIiIiIipEF59fVCmKf+PwDUz1TNXuKz4lA47zz2LXx2N9E3ztPls8HzilNepWyb2gnZSUhGnTpokTKgDAysoKmzZtwldffaV2XkREREREVL6UqMK4vb09+vfvjyZNmsDf3x+tWrXCyJEjxfM///wzpk6dihkzZhRjlkREREREREREZdeh8EP4/vfvlWL/+/J/GPD5ALX7uvU0Hn3W/64SPzDWBY42ZtDSyvv6HUOHDsXhw4fF4x49emDTpk2oXLmy2nkREREREVH5U6IK4wCwZs0aBAUFQU9PT+Xc69evsXnzZvTo0aMYMiMiIiIiojKFa6kTESmJTYvFfy78B7ff3laK3xx8EwZSA7X7O/fgDbx3BKvEq5jqo46tudr9/fDDDzh58iR0dHSwatUqeHt7QyLhf8yJiIiIiChvSlxhHIDS0un/NG3atCLOhIiIiIiIiIio7At+HYzhgcNV4ls7b9WoKL7qt7+w6rdwpZiejhaQnvc+FAoFtLS0xGMHBwfs3LkTzZo1Q61atdTOiYiIiIiIyjet3C8hIiIiIiIiIqKyasf9HSpF8TbWbXB90HU4VXFSu7/+G6+pFMW3D3eCuaFuntorFAqsXLkSLVu2hEwmUzrn6enJojgREREREWmkRM4YJyIiIiIiKmwSrqVOROVcZlYmJgVNwpXoK0rx+S3no2ftnhr16bbsAqJiU5RiA5vboG3dSgj/RJt/evbsGYYNG4agoCAAwNy5c7F48WKNciEiIiIiIvonFsaJiIiIiIiIiMqZuLQ4tN3fViV+stdJ2JjYaNTnrutPVYri12e1RxVT/VzbCoKAPXv2YPz48UhMTBTjmZmZEASBe4kTEREREVG+sTBORERERETlEmssRFRevU19i56HeyrFpBIpzvQ9AytDK436tJt5QiUW9G3bPBXF4+LiMGbMGAQEBIgxGxsbbN++He3atdMoHyIiIiIion/jHuNEREREREREROXEwfCDaP9reyRlJomxr7/4Gn8O/VOjorggCNkWxWd3/wL2lhVybX/69Gk4ODgoFcWHDBmCu3fvsihOREREREQFijPGiYiIiIiIiIjKOIWgwKgzo3Dj9Q2l+LfNvoVXfS+N+hQEAfazTqrE78zpBFMDnVzbTp48GX5+fmLMwsICGzZsQL9+/TTKh4iIiKisEwQBcrkcWVlZxZ0KUb5pa2tDKpUW6bZJLIwTEREREVG5xJXUiai8uPTiEsafG68S/6/TfzHkiyFq9ZWlELD45ANsvhKV7fnwhV2go537AoUSiQQmJibicefOnbF161ZUq1ZNrXyIiIiIyouMjAy8evUKqampxZ0KUYExNDRE1apVoaurWyT3K9GF8Xfv3mH58uVYuHAhIiIiMG3aNCgUCvz444+oV69ecadHRERERERERFRiZWZlwuOwB14kv1A5d8nzEsz1zdXqTxAE1PpOdYb4R098u6nV3/fff49Lly5h4MCBGDNmTJHOFCEiIiIqTRQKBaKioqCtrY1q1apBV1eXvztRqSYIAjIyMhATE4OoqCjUqVMHWlqFvwN4iS6MDxkyBDo6H5bemjBhAqysrKClpQVvb2/8/vvvxZwdERERERGVavwMgYjKMEEQ0PVQV7xOea0UtzKwQmDfQOho5bzUeXayWzYdAMwMdXDbp2OObR89eoRjb97gn6VzXV1dXLx4kR/qEhEREeUiIyMDCoUCNjY2MDQ0LO50iAqEgYEBdHR08PTpU2RkZEBfX7/Q71miC+OXLl3CgwcPkJ6ejitXruDt27d4//49ateuXdypERERERERERGVSM8Sn6HbIdXZ20H9g2BpYKlRn3YzT6jEwn7oDEPdnD9aEgQBP/30E6ZPnw55ejpq1rCFwz/OsyhORERElHdFMaOWqCgV9Zgu0YVxKysrXL9+Henp6WjUqBEMDAxw6dIlVK5cubhTo395/z4eT6KiYGtnB3Nzi+JOh4iIiIiIiKhcevjuIfod66cUM9Mzw+UBlzXqTxCEbGeK52Uv8ZcvX2LEiBEIDAwUY+vjYvGTra1GuRAREREREeVHif5qyfz58zF48GCMHTsW//vf/3Dt2jX06tULM2bMKO7U6B9OnTyB7l06YdGCH+DewQ2nTn74Fnl4+F8Y1L8PXJ2dsGLZEgiCUMyZEmmO45nKIo5rKqs4timvJCXgDxFRQToYflClKN6yekuNi+Iv36dlWxR/tMA916L4/v370aBBA6Wi+JBq1bCkajWNciEiIiKiskmhUBR3CmpZtWoVEhMTizuNEsvX1xfp6enFncYnlejC+JAhQxAfH4/Y2Fh06dIFdevWRUhICMaMGVPcqdH/S0xMxJJF87Ft5x7sP3AYPnPmYfWKZcjIyMCk8WNQr3597P3lACIjInDk8MHiTpdIIxzPVBZxXFNZxbFNRETl1YG/DmDO73OUYnOd52JDhw0a9Tfh59tw8T2vEo9a3BV6Uu1Ptnv//j2GDBkCT09PxMfHAwCqVq2K06dP4/vadWDA5T+JiIiIypWsrCzMmDEDsbGxAIDFixfD29sbAJCQkIDGjRvjwYMHSm38/f2hpaUFqVQqvipVqqTSd+/evbFp06bCf4j/t2PHDpw+fRoVKlQosnuWNnK5HGPHji3uND6pxL8bqVChgrjZuoWFBT777LNizoj+KTUlBdNnfoc6dT78c6lb93MkJibgyuVLSE5KxrczZsGmRg1MnDwNhw4EFHO2RJrheKayiOOayiqObSrLjhw5gpo1a0IqleLLL78UPzgIDQ2Fk5MTzM3NMX36dKVVEgrjHBGVPAf+OoC51+Yqxda4rUGfz/po1J/dzBM4fveVUsyigi6e+HbLcU/wc+fOwcHBAXv27BFj/fv3R2hoKDp37qxRLkRERERUumlpaeHdu3dwd3dHYmIidHR0YGBgAAAYP3487OzsVGp/UqkU/fv3h1wuh1wux8OHD6GjowMAOHbsGFxdXQEAurq6Yjw3AQEBsLW1RbVq1bB37161nyM2NhYLFy7E3r17oa399xdFZ8+erZL/sGHDMGXKFPE4JCRE6ffoe/fuwcnJCcbGxujRowfevn2rdj7q0OTZBUGAqakpJBKJ+FqwYEGu53x8fPD8+XMEBQUV2vPkR4kujEdGRmLw4MEQBAHBwcFo2LAhHBwccOXKleJOjf5flapV0a27BwAgMzMTO7ZvRfsOnfDXo4do+P/7wgPAZ3XrIjIiojhTJdIYxzOVRRzXVFZxbJM6JJLif+VVREQEhg8fDl9fX0RHR8PW1hYjR46ETCZDjx490LRpUwQHByMsLAzbt28HgEI5R0Qlz5izY1SK4lcGXIFbDTeN+ttz46lKzMnOHLdnd8yxnVwux4QJE/DixQsAgKmpKfbs2YN9+/bBwsJCo1yIiIiIqHRLS0tDZmYmNmzYAAsLCzx9+vfvms+fP0d4eDj27NkDQRCQlpYmnsvuy5gfY1KpVKkwnRehoaEYPHgwZs+ejcDAQHz//fd49OiRWn2sXr0a48ePh7m5uVL8zJkzCA8PV3q2nKSkpKBLly7o2LEj7t69i4oVK2LUqFFq5aIOTZ89PDwc5ubmiI+PF1/Tp0/P9RwALF++HD/88EOhPVN+lOjCuJeXF0xMTCCRSDB16lR069YNHh4eGD9+fK5tZTIZEhMTlV4ymawIsi6fHj18iHatW+La1auYPvM7JCcno3p1a/G8RCKBtrYWEhMSijFLIs1wPFNZxHFNZRXHNpVVDx48wKJFi9C/f39UrlwZY8eORXBwME6dOoWEhASsWLECtWrVwqJFi7BlyxYAKJRzRFSydD3YFVdfXlWKbey4EaZ6pmr3JQgC7GaewP8OhSrFHy/sgl/HuOTaXiqVYufOndDW1ka7du1w7949DBo0KMcZ5kRERERUtg0aNAg1atTA559/jsjISLi5uWH69OnYvXs32rZti7i4ODg6OqJWrVro2bOn2E4ul+PEiROoXbs2ateujfbt2yMzMxMAxBnK6ti8eTPc3NwwcuRIODg4YMKECdi1a5dafRw+fBgDBw5UisXHxyMkJASurq44e/ZsnvrZv38/jI2NsWjRItjb22POnDk4cuQIUlJScmxnaWkJMzMzlVdISEiO7TR99ps3b8LZ2VnpXnp6ermeA4BGjRrh3bt3hT4TXhMlujB++/ZtzJ49G8nJyQgJCcH8+fMxYcIEPH78ONe2ixcvhqmpqdJr6ZLFRZB1+fRZ3brw37odtWrXxvc+s6CtrQ0dXV2la3T19JCWnl5MGRJpjuOZyiKOayqrOLZJHZIS8Mqr7t27Y8yYMeLxo0ePULt2bdy5cwctWrSAoaEhAKBhw4YICwsDgEI5R0Qlx38v/RfPk54rxS57XoZLtdyL2P8mCALsZ51UiR8a5wKpdvYfHcnlcsTExCjFnJyccPXqVZw9exY2NjZq50FEREREZcuhQ4fw+vVrPH78GKdOnYKJiQk6dOiAIUOGYPTo0fjiiy9w/vx5PH36FIGBgWK7AQMG4N69e+jWrRs8PT1x8eJFBAcHa5zHnTt30K5dO/G4efPmuH37dp7bZ2ZmQiaTqexzfu7cOdSrVw/u7u55LoxfvXoVLVu2FI9tbGwwevToXAvjt27dQkhIiMrriy++yLGdps9+8+ZN3Lx5E2ZmZqhUqRJ8fHzEbdZyOveRk5MTQkNDs+u6WEmLO4Gc2Nra4pdffkF6ejqcnZ0hlUpx/vx52Nra5tp21qxZmDZtmlJM0Nb7xNWUXxKJBPXqfYH5C33h3tENk6ZMw+PH4UrXpKak5HmvB6KSxNTUlOOZyhyOayqrOLaptJHJZCorW+np6Sl90/rfMjIysGzZMkydOhWRkZGwt7cXz31YJUEb8fHxSExMLPBz/14yjoiK3v24+/C54oPH75UnDdwdelej2dlZCgG1vlMtip+a3Ar1qppk2+bx48f4+uuvAQCXL1+GVPr3x0tffvml2jkQERERkWZ6+F1BTFLRrpZsZayHYxNd83x9RkYG/P39sWDBAqxduxaRkZGIiIjApEmTEBcXh0aNGmHx4sUYPXo0JBIJsrKyIAgCqlSpgvT0dJiYmKBKlSoAIM4aV9e/3+eamJggOjo6z+1jY2NhaWmpEj9z5gycnZ3h7OyMlStXQqFQQEsr5znJ0dHRaNKkiXgslUqxYcOGXHPIS200O5o+e0REBHr16oXJkyfj8ePH6NevHxwcHODp6ZnjuY+srKxK5IzxEl0YX7VqFby8vGBoaIi9e/fi/PnzGDlyJPbs2ZNr2+w+TEqXF1am5deN69dw9colTPv2vwAAbemHfR3sa9bEoQMB4nXR0S+QkZEBU1P1l3MjKm71GzhwPFOZw3FNZRXHNpU2ixcvxrx585Ric+bMwdy5cz/ZxsfHB0ZGRhg1ahR8fHxU3vfo6+sjNTUVUqm0wM+xME5UvH7961f8cE11r77bQ25rVBTf/8dzzDhwVyUeuagrtLRU+xMEAf7+/pg2bRpSU1MBfPjv2OzZs9W+NxERERHlX0ySDK8TS+4qeUeOHMHYsWPh5OSEa9euwd7eHn5+fjA3N4e+vj6WLFkCd3d3eHp6wtraGt27d8fDhw/RpEkTGBgYIOX/Jzv4+fkhIyMDAwcORL9+/dTO49/vcz++x80rIyMjJCUlqcTPnj2LuXPn4ssvv8T79+9x+/ZtNGvWLMe+MjMz1d4jPT80ffYTJ06If7exscHEiRMREBAAT0/PHM99lJSUBCMjowJ6ioJTogvjnTp1wqtXr8Tj1NRUxMTElMgfZHllX7Mmpk4ajxo17ODaqjXWrlkFZ5eWaNW6LebNmY1jRw6jx1c9sXWzP75s4VKk/7ITFZSmzZyQlJzE8UxlCsc1lVUc26SWErDtbXYrXeU0W/zs2bPYsGEDrl+/Dh0dHVhYWKgsTZaUlARdXd1COUdExedB3AOVoni1CtVwvPdx6GipvzKK3cwT2cajFnfNtsj++vVreHt74+TJv2eX16pVCx06dFD73kRERERUMKyMi36lZHXu6eLigsOHD6N58+ZizNDQEJcuXRKP3dzc8PDhQ3E7r/r164srq7Vo0QLfffcdPDw8sH79ety9excKhQJZWVlq5WxhYaG0DZC673GNjY2RlJQEuVwurpYUHh6OJ0+eYPz48Zg8eTIUCgXOnj2ba2HczMwM8fHx4nFmZiZ0dXURFhaGevXqfbKdpaUl5HLVGcAXLlxA48aNP9kuv8/+z7xfvHiR53ORkZEaz3IvTCW6MP5vH/+liImJgZWVVTFnQwBQqVJlLF2xGsuWLMaKZUvg0tIVC32XQiqV4vu58zFrxn+wYvmPUGRlYcuO3cWdLpFGOJ6pLOK4prKKY5tKm9yWTf+nyMhIDB48GOvXrxf3EHNycsLmzZvFa548eQKZTAYLC4tCOUdExUMhKOAd6K0U29hxo0b7iQPZF8UrVtDFlf+2y7YofvDgQYwaNQpxcXFibPTo0Vi2bBknLxAREREVI3WWNC8OXbp0watXr5S2uHv9+jVkMhns7OzEWFZWFjIyMhAeHg4Tkw/b+cTExCA0NBRNmzYFAKSnp8PAwAAymQzp6erNkndycsL169fh7f3hd+qQkBBUr15drT5cXFxw8eJFtG/fHsCHL67b2dkhKCgIALB06VKcPXsWs2bNgrm5uVLx+/379+IKbI6Ojjh27Jh4LiIiAhKJBNbW1jne/9atWyr7eANAtWrVcmynybOnpqbC2dkZN2/eFD+z+OOPP2BnZ5fjuY+Sk5MREREBBweHHO9THEp0YTwsLAzffvstwsPDxW9/CIKAly9fquzDR8WnpWsrtHRtpRJv174Djp48g7D799CocRN+kEalGsczlUUc11RWcWxTXklKwpTxPEpLS0P37t3Rs2dPfPXVV0hOTgYAtGrVCgkJCdi5cyeGDh0KX19fdOjQAdra2mjdunWBnyOi4tFoZyOl45+7/gwHK80+ZMquKH54fEs0tjFTiSckJGDy5MnYsWOHGKtcuTK2bNmCbt26aXR/IiIiIio/goODlY7Dw8PRsmVLNGjQAK1bt/7kNmKCIGDq1Klo166dWMRNT0+HoaEhevXqhV69eqmVR58+feDq6oqpU6fC1tYWfn5+GDRokFp9jBkzBnPnzhUL42fOnEGHDh3EgrCHhwc2b96M1NRUuLm5YejQobhw4QLs7OywZMkStG3bFgDg5eUFX19fLF26FH379sWsWbPQqVMnGBsb53h/TWdf5/TsCoUCiYmJMDU1VfqCrKGhISpWrIiJEydi3LhxuHz5Mn755RecO3cux3MfLVu2DF5eXhrlW9hKdGF8+PDhcHZ2RtWqVZGYmIgRI0Zg2rRp8PX1Le7UKI8qV66MypUrF3caRAWC45nKIo5rKqs4tqmsCQwMxIMHD/DgwQNs2rRJjEdFRcHf3x+DBg3C9OnTkZWVhYsXLwL4sIJCQZ8joqIlCAIGnhioFPvB5Qe1i+KCIODx22R0XHlJ5dz9eZ1RQU/146Hk5GQ4OjoiKipKjPXq1QsbN27kKn5EREREpLbg4GAMGjQIvr6+6N+/P7p27YqIiAgsX74clSpVEq+LiYnBmDFjEBwcjKtXr+Ldu3f49ddfceDAAYwYMQIA8OLFCwQHB+PWrVvo06dPrvdu1KgRJkyYAEdHRxgaGsLe3h7jxo1TK//WrVujSpUqWLduHUaPHo2goCBs2LBB6bxEIsHFixfh4eGBqVOnYsiQIUhKSkLLli3h5+cHALC2tsbJkycxfvx4zJs3D+3bt1f6ImpBy+nZnz17Bnt7e8THx8PMzEyp3fbt2zFs2DC4urqiTp062LdvH1q3bp3rueDgYAQEBKh8KaKkkAjZzbsvISpUqICoqChERUVh4sSJuHnzJq5evYpx48bhzp07aveXrrr0PhERERER5ZN+if667ac9jSv+VahsKxbMfnDR0dEIDg6Gi4uLSsGqMM4RaeLjTISEhARxeUTK2ZKbS7D7gfKWIPe87uWpbZZCwHcH7+GX4OefvOZT+4l/NG3aNKxcuRLGxsbw8/PD0KFDc7w+N+Ft2kL+5g2klSujzsULGvdDREREVN6kp6cjKioK9vb20NfXL+508iwkJAQ3btxAQEAAwsPDsWrVKvTs2RPAh721fXx8sH79enTv3h3dunXDwIEDcfnyZcyYMQMBAQGwsbGBXC5Hx44d0aJFC8yePRuGhoYICwtDx44d0apVK6xfv15cpjw3d+/eRXR0NNq1a5fnbc3+KSkpCZ07d8bx48dL3QqF+X32vOrcuTOWLVuW52XUsx3bfwUCP/dHokyAqW9Sgb6HLNGFcUdHR3h6emLKlCmoXr06Hj16hPj4eDRt2hSJiYlq98fCOBERERFRwSuthfFn74q/MF7DovDejBKVNCyMq+e7y9/hWOTfew/qaOng9te389RWEATYzzqZ4zWPF3aBVFsrx2vS09MxYcIE+Pj4KO0ZqCkWxomIiIg0U1oL45MmTcLz58/Ru3dveHp6QldXV+WayMhIrFu3Dtra2vjxxx+LIUv1ZGVlcauxHKj78ynqwniJ/ghrzZo16NevH0aMGAFvb2/UrFkTEokEX331VXGnRkRERERERERU4J4mPkX3Q92VYh1tO2JF2xW5tk2RyTEj4C5O3HuV7fm2da3g2cwGXRyqKsWzsrKwYsUKGBsbY8yYMWJcX18fmzdv1uApiIiIiIg+1PlyU7NmTSxfvrwIsikYLIrnrKT/fEp0YbxVq1Z49erDm7kff/wR3bp1Q3JyMtzd3Ys5MyIiIiIiKu00XwyYiKhw3I+7jwHHB6jEcyuKKxQCOq+6hPC3ydmef+Lb7ZNtnzx5gqFDh+Ly5cvQ19dH27Zt8fnnn6uXOBERERERUSlQogvjAJT2rmrTpk0xZkJEREREREREVDiCXwdjeOBwpdhn5p8hoEdAju2+2RmMs2FvPnk+clHXbOOCIGDHjh2YNGkSkpKSAAAymQxBQUEsjBMRERERUZlU4gvjRERERERERERl2YG/DmDutblKsVnNZ2FQvUE5tmvpex7R79NU4mPb1sKUDnWgJ81+GcOYmBiMGjUKhw8fFmN2dnbYuXMnWrVqpXb+REREREREpQEL40REREREVC5JuJY6EZUAf7z+Q6UovrHDRrhUd8mx3ehdwSpF8W4OVbF2kKPS6nv/dvz4cXh7e+Pt27dibPjw4Vi1ahVMTEzUfwAiIiIiIqJSosQVxrW0tHJ8AycIAiQSCbKysoowKyIiIiIiIiKigpWUkYQRgSOUYkvbLM2xKB4anYDufldU4hGLukJb69OfpyQnJ2PatGnYtGmTGLO0tMSmTZvQs2dP9ZMnIiIiIiIqZUpcYTwqKqq4UyAiIiIiIiIiKlRyhRwue5UL4Md7HYetie0n2ySlZ2ZbFL/xXfsci+IAoFAocObMGfG4e/fu2Lx5MypXrqxm5kREREREBUehUEBLS6u408izVatWYcSIEVxt6RN8fX0xZcoU6OvrF3cq2SpxI83W1jZPLyIiIiIiovyRlIAXEZVHL5JewHGXo1LM50ufHIvizRachcPcMyrx01NaobJJ7h86mZiYYOfOnTAxMcGmTZtw9OhRFsWJiIiIqNBlZWVhxowZiI2NBQAsXrwY3t7eAICEhAQ0btwYDx48UGrj7+8PLS0tSKVS8VWpUiWVvnv37q20IlJh27FjB06fPo0KFSoU2T1LG7lcjrFjxxZ3Gp9U4grjRERERERERERlVVhcGLoc7KIUq2dRD56fe2Z7fYpMDruZJxCbnKEU7+ZQFU98u+HzKtnPVAkLC8OzZ8+UYq1bt8bTp08xcuTIHLexIyIiIiIqKFpaWnj37h3c3d2RmJgIHR0dGBgYAADGjx8POzs7fPbZZ0ptpFIp+vfvD7lcDrlcjocPH0JHRwcAcOzYMbi6ugIAdHV1xXheXLt2DXXr1tXoOWJjY7Fw4ULs3bsX2traYnz27Nkq+Q8bNgxTpkwRj0NCQpR+/7537x6cnJxgbGyMHj164O3btxrllFcBAQGwtbVFtWrVsHfv3jy3W7duHSpXroyaNWvi/PnzeTrn4+OD58+fIygoqMDyL0glsjAuk8lw6tQp/P7772Js48aNOHXqFNLS0ooxMyIiIiIiKiskkuJ/EVH5cuCvA/A8rlwAH1JvCPb32J/t9T/feIb6cwJV4t92+gzrBjfJto1CocCqVavQpEkTDBs2DAqFQum8mZmZZskTEREREakpLS0NmZmZ2LBhAywsLPD06VPx3PPnzxEeHo49e/ZAEASl+l92X+L8GJNKpUqF6by6ffs2evXqBZlMpsGTAKtXr8b48eNhbm6uFD9z5gzCw8OVni0nKSkp6NKlCzp27Ii7d++iYsWKGDVqlEY55UVoaCgGDx6M2bNnIzAwEN9//z0ePXqUa7vAwEB8++238Pf3x+7duzFy5EjExcXleg4Ali9fjh9++KHQnik/Stwe47du3YKHhwd0dXUxfvx4uLh82G9r586diIqKglwux+nTp9GkSfZvAImIiIiIiIiIShqvU164/fa2Umy7+3Y0rdw02+tH7vgDvz1QnTnycL479HWy/yDw+fPnGDZsmDhjIygoCFu3bsXIkSPzmb16Ek+fRswaPyhSUpTi8piYIs2DiIiIiIrXoEGDcO3aNRgZGQEA3NzcEBcXB1NTU5w6dQoSiQSOjo7IzMzE559/jsDAD18KlcvlOHHiBGrXrg0AyMzMRGZmJoAPBXJ1Vz9KSUlBr169MG7cOGzdulWjZzl8+DDOnTunFIuPj0dISAhcXV1x9uzZPP3evX//fhgbG2PRokUAgDlz5qBmzZpISUnJcYl2S0tLyOVylfiFCxfQuHHjT7bbvHkz3NzcxNwmTJiAXbt2YcGCBTnmuX79enh5eeGrr74CAHz11Vc4dOgQRo4cmeM5AGjUqBHevXuHt2/fZrsEfnEqcYXx0aNHw8fHR2X9+atXrwIAFixYgNGjR+OPP/4ojvSIiIiIiIiIiNSyPXS7SlF8h/sONKms+qX/9MwsfD77tEp8YHMbLO7dMNv+BUHAzz//jPHjxyMhIUGMT5kyBYMHD85n9uqLWeOHjMjIT57X4p6MREREROXCoUOHxL+Hh4ejc+fOcHR0RN26dVGjRg1cuXIFa9euRY0aNZTaDRgwAB07dsTKlSthZGSEb775RqNZ4h/p6Ojg999/R3h4uEaF8czMTMhkMpUi77lz51CvXj24u7vnuTB+9epVtGzZUjy2sbHB6NGjcy2M37p1C4IgqMSrVauW4/3u3LmDLl3+3sqpefPmmD9/fq553rlzB4MGDVJqd/ny5VzPfeTk5ITQ0FC0a9cu13sVpRJXGH/48CE6der0yfODBw/G4sWLizAjIiIiIiIqi7iSOREVBUEQsPzWcqXY3aF3PznLJbui+JmprfFZZeNsr3/37h3Gjh2L/fv/Xo7d2toa27dvR/v27fORuebEmeJaWpBaWSmd06pQAVaTJhVDVkRERERUHDIyMuDv748FCxZg7dq1iIyMREREBCZNmoS4uDg0atQIixcvxujRoyGRSJCVlQVBEFClShWkp6fDxMQEVapUAQBx1ri6dHV1Ub16dYSHh2vUPjY2FpaWlirxM2fOwNnZGc7Ozli5ciUUCgW0tHLexTo6OlppVWypVIoNGzbkmoOtra36iQNITEyEvb29eGxiYoLo6Oh8tctLn1ZWVoW+d7omSlxh3N3dHZMmTcK2bdtUvnmRnJyM//3vf2jbtm3xJEdERERERERElEcJsgS47nNVigUPCf5kUbyl73mV2OOFXSDVzv7DtcDAQAwfPhyvXr0SY0OGDIGfn1+J2EtcamWFOhcvFHcaRERERGXXxjZAchEXH40qAaMv5unSI0eOYOzYsXBycsK1a9dgb28PPz8/mJubQ19fH0uWLIG7uzs8PT1hbW2N7t274+HDh2jSpAkMDAyQkpICHR0d+Pn5ISMjAwMHDkS/fv0K+QFVGRkZISkpSSV+9uxZzJ07F19++SXev3+P27dvo1mzZjn2lZmZma/Z7+qSSqXQ09MTj/X19ZGampqvdnnpMykpSVxCvyQpcYXxLVu2YNiwYahWrRpq1qyJypUrQ1tbG/Hx8fjrr79Qv359HDt2rLjTJCIiIiKiUk7NLcmIiNQSmRCJ0WdHK8X+9+X/oKetl+31z9+lIvp9mlIsPIei+P379+Hu7i4em5ubY8OGDejfv38+MyciIiKiUiP5LZD0sriz+CQXFxccPnwYzZs3F2OGhoa4dOmSeOzm5oaHDx/C0NAQAFC/fn3IZDIAQIsWLfDdd9/Bw8MD69evx927d6FQKJCVlVWkz2FsbIykpCTI5XJIpR9Kq+Hh4Xjy5AnGjx+PyZMnQ6FQ4OzZs7kWxs3MzBAfHy8eZ2ZmQldXF2FhYahXr94n22m6x7iFhQViYmLE46SkJOjq6uaYY27t8tJnZGSkxrPcC1OJK4ybmpri0KFDePLkCa5evYro6GhkZmbCzMwMjo6OcHFxKe4UiYiIiIiIiIg+Kfh1MIYHDleKNa3cFAM+H/DJNq1+DFI6vj27I3Q+URQHPnxg+M0332DTpk3o3Lkztm7dmuv+gkRERERUxhhVyv2aYrxnly5d8OrVK+jo6Iix169fQyaTwc7OToxlZWUhIyMD4eHhMDExAQDExMQgNDQUTZs2BQCkp6fDwMAAMpkM6enpBfMsanBxccHFixfF7YrOnj0LOzs7BAV9+D1+6dKlOHv2LGbNmgVzc3Ol4vf79+9hbm4OAHB0dFSaABwREQGJRAJra+sc76/pHuNOTk64fv06vL29AQAhISGoXr16rs/7sd3H5/1nu5zOAR9WAI+IiICDg0Ou9ylqJa4w/pGdnZ3SvxRERERERERERCXd7y9/V5kp3sW+C35s/eMn25x/+EbpeKSrPSwqKM+4kMvl0NbWVlqGfcWKFXB2dsawYcM+uTw7EREREZVheVzSvLgEBwcrHYeHh6Nly5Zo0KABWrdujblz52bbThAETJ06Fe3atRMLrunp6TA0NESvXr3Qq1evwk5dxZgxYzB37lyxGHzmzBl06NBBrGV6eHhg8+bNSE1NhZubG4YOHYoLFy7Azs4OS5YsEbeJ9vLygq+vL5YuXYq+ffti1qxZ6NSpE4yNjXO8v6azr/v06QNXV1dMnToVtra28PPzw6BBgwAACoUCiYmJMDU1VXk/0bdvX4wdOxbe3t7Q0tLCli1bsGrVqlzPAcCyZcvg5eWlUb6FLecd4ImIiIiIiMooSQn4Q0RlR2pmKr4++bVKUfygx8Eci+IKhYAR25U/MPTp/oXS8aNHj+Ds7Izt27crxY2MjDB8+HAWxYmIiIioxAsODka3bt3g6+uLo0eP4vz58/j666/x9q3yHukxMTHo27cvLl++jJ9++gnv3r3Dxo0bceDAAXF29IsXL3D48GHcunULFSpUKJL8W7dujSpVqmDdunWQy+UICgpCu3btlM5LJBJcvHgRHh4emDp1KoYMGYJGjRpBIpHAz88PAGBtbY2TJ09i165dcHBwgEKhwI4dOwot70aNGmHChAlwdHSEtbU1BEHAuHHjAADPnj2Dubk5EhISVNr16NEDbdq0Qe3atVGzZk04ODigd+/euZ4LDg5GQEAApk2bVmjPlB8SIbt592VUuurS+0RERERElE/6JXYdqpy9Tsgs7hRQxVQn94uIyoiPMxESEhLE5RHLittvbsPrtOqMiKVtlsLdzj2bFh9c+isGQ7feVIodGucCxxofllkUBAE//fQTpk+fjrS0NBgZGeHu3buwt7cv2AcoYOFt2kL+5g2klSujzsULxZ0OERERUamXnp6OqKgo2NvbQ19fv7jTybOQkBDcuHEDAQEBCA8Px6pVq9CzZ08AH/bW9vHxwfr169G9e3d069YNAwcOxOXLlzFjxgwEBATAxsYGcrkcHTt2RIsWLTB79mwYGhoiLCwMHTt2RKtWrbB+/XpxmfLClpSUhM6dO+P48eOwsLAoknsWlLt37yI6Ohrt2rWDnp5enttdu3YNKSkpaNeuHbS0tHI917lzZyxbtizPy6hnO7b/CgR+7o9EmQBT36QCfQ/JwjgREREREeULC+OaY2GcypOyWhiPfB+Jr458pRL/pfsv+KLiF9m0+GD0rmAE3ldeQr2yiR5ufNcBAPDy5UuMGDECgYGB4vnPPvsM+/fvR6NGjQoo+8LBwjgRERFRwSqthfFJkybh+fPn6N27Nzw9PaGrq6tyTWRkJNatWwdtbW38+OOnV1oqKbKysqCtrV3caZRY6v58irowXko/wiIiIiIiIsonrjxMRPkgCAI239uMNX+uUYoPrjcYM5vPzLHt7utPVYrijW3McHh8SwDA/v37MWbMGMTHx4vnx48fjx9//BGGhoYF9ARERERERIVrzZo1uV5Ts2ZNLF++vAiyKRgsiuespP98WBgnIiIiIiIiIlJDdHI0eh3phTR5mlJ8Q4cNaFm9ZY5t0zOz4HM4VCl2anIr1Ktqgvfv32PChAnYs2ePeK5q1arYunUr3N0/vSQ7ERERERER5Y6FcSIiIiIiKpc4YZyINHE/7j4GHB+gEs9t6XQAGLkjGL89UJ4pfnpKK3xexQQhISHo0aMHXrx4IZ7r168f1q9fj4oVKxZM8kREREREROUYC+NERERERERERHmQmZWJgccHKsVcqrlgtdtq6Euz3+vx7ov38Fh7Ndtzze0s8HmVD3vl1ahRA1lZWQAAU1NTrFu3DoMGDYJEwq/xEBERERERFQQWxomIiIiIiIiIchGXFoe2+9sqxZa3WY5Odp0+2WbmgbvY98fzbM9N71wX491qi8cWFhbYtm0bli5dim3btsHGxqZA8iYiIiIiIqIPWBgnIiIiIqJyiZMwiSgv0uXp6HesH54kPlGKj3QY+cmi+Nrz4Vh25q9P9hkyuz02/eSH1/W+RpUqVcR4586d0alTJ84SJyIiIiIiKgQsjBMRERERERERZSNLkQWnPU4q8T51+mByk8lKsbhkGdqvuIj3qZnZ9vXzN1/CpZYlIiIi0LVjO1y7dg0XLlzA8ePHlQrhLIoTEREREREVDq3iToCIiIiIiKg4SErAHyIq2RrvaqwS82vnh7kuc8XjpPRMzAi4g6YLfvtkUfzU5FZwrlkRmzZtQqNGjXDt2jUAwOnTpxEcHFwYqRMRERERlQoKhaK4U1DLqlWrkJiYWNxplFi+vr5IT08v7jQ+iYVxIiIiIiIiIqJ/SMlMgcMOB5X4Pa97aGvTFgDw841nsJt5Ag5zz2B/8AuVa80NdRD2Q2c88e0Gc0kqPDw8MGrUKKSkpAAAatWqhcuXL8PJSXVGOhERERFRWZGVlYUZM2YgNjYWALB48WJ4e3sDABISEtC4cWM8ePBAqY2/vz+0tLQglUrFV6VKlVT67t27NzZt2lT4D/H/duzYgdOnT6NChQpFds/SRi6XY+zYscWdxiexME5ERERERERE9P+eJz5Hi59bqMTved3D83ep+GrdVdjNPIHvDt3Ltn3bulaIWtwVf37fCYa6Uhw6dAgODg44fvy4eM2oUaMQEhICFxeXQnsOIiIiIqKSQEtLC+/evYO7uzsSExOho6MDAwMDAMD48eNhZ2eHzz77TKmNVCpF//79IZfLIZfL8fDhQ+jo6AAAjh07BldXVwCArq6uGM/NvHnzYGFhAT09PXh6eiIpKUmt54iNjcXChQuxd+9eaGtri/HZs2er5D9s2DBMmTJFPA4JCVHaMunevXtwcnKCsbExevTogbdv36qVi7oCAgJga2uLatWqYe/evXlqIwgCTE1NIZFIxNeCBQtUrouMjIShoaF47OPjg+fPnyMoKKjA8i9ILIwTEREREVH5JCkBLyIqUTKyMtD1UFeVeDPBH3YzT6DVj0G48/x9tm3HtKmF27M7Yvvw5pBIJEhMTMTw4cPRu3dvcXZM5cqVcezYMWzcuBFGRkaF+ShERERERMUuLS0NmZmZ2LBhAywsLPD06VPx3PPnzxEeHo49e/ZAEASkpaWJ5/5ZRP53TCqVKhWm82LPnj3YvXs3Tp8+jfv37+POnTvw9fVVq4/Vq1dj/PjxMDc3V4qfOXMG4eHhSs+Wk5SUFHTp0gUdO3bE3bt3UbFiRYwaNUqtXNQRGhqKwYMHY/bs2QgMDMT333+PR48e5douPDwc5ubmiI+PF1/Tp09XuW7MmDFK/+wAYPny5fjhhx8K7BkKkrS4EyAiIiIiIiIiKm5Ziiw03d1UKSaL6YCM2A4Iwrts2xjrS3FpuhvMK+iqnDt37hy2b98uHvfq1QsbN26ElZWVRvklnj6NmDV+UPz/UuwlmTwmprhTICIiIqISYNCgQbh27Zr4pVA3NzfExcXB1NQUp06dgkQigaOjIzIzM/H5558jMDAQwIfluE+cOIHatWsDADIzM5GZmQkA4uxldTx79gzbt29H8+bNAQCenp64fv26Wn0cPnwY586dU4rFx8cjJCQErq6uOHv2LEaOHJlrP/v374exsTEWLVoEAJgzZw5q1qyJlJSUHJdot7S0hFwuV4lfuHABjRs3/mS7zZs3w83NTcxtwoQJ2LVrV7azv//p5s2bcHZ2hpmZ2Sev2bVrF54/f64Sb9SoEd69e4e3b99muwR+cWJhnIiIiIiIiIjKteSMZDjvdVaKZSY0RkZsh2yvXz2gMbo3rAZtrU9/INerVy8MHjwYR48exZo1a+Dl5aX2B3j/FLPGDxmRkRq3Lw5a3HuRiIiIqFw7dOiQ+Pfw8HB07twZjo6OqFu3LmrUqIErV65g7dq1qFGjhlK7AQMGoGPHjli5ciWMjIzwzTffqD1L/J9mzZqldPzo0SOx6J4XmZmZkMlkKkXec+fOoV69enB3d89zYfzq1ato2bKleGxjY4PRo0fnWhi/desWBEFQiVerVi3H+925cwddunQRj5s3b4758+fnmufNmzdx8+ZNmJmZQVdXF6NGjcL8+fPF9zRxcXGYPn06Dh48qPQ8Hzk5OSE0NBTt2rXL9V5FiYVxIiIiIiIql7iSOREBQLo8HS33KX+QI2TpIf3lAABAM1tzTOnwGUwNdFDTqgIq6GX/UcqzZ89UPtBbu3Yt5s+fD3t7+3znKc4U19KCVMNZ50VJq0IFWE2aVNxpEBEREZVpnsc9EZsWW6T3tDSwxC/df8nz9RkZGfD398eCBQuwdu1aREZGIiIiApMmTUJcXBwaNWqExYsXY/To0ZBIJMjKyoIgCKhSpQrS09NhYmKCKlWqAIA4azw/Hj16hEOHDiE4ODjPbWJjY2FpaakSP3PmDJydneHs7IyVK1dCoVBASyvnXayjo6PRpEkT8VgqlWLDhg255mBra5vnfP8pMTFR6f2IiYkJoqOjc20XERGBXr16YfLkyXj8+DH69esHBwcHeHp6AgCmTZsGT09PuLi4ZNveysqq0PdO1wQL40RERERERERULvn96Qf/u/5KMVmsGzJiOgMAIhd1hVYOs8IBICsrCytWrICPjw927twpflAEAGZmZjkuPagJqZUV6ly8UKB9EhEREVHpFJsWi7epJa/4+NGRI0cwduxYODk54dq1a7C3t4efnx/Mzc2hr6+PJUuWwN3dHZ6enrC2tkb37t3x8OFDNGnSBAYGBkhJSYGOjg78/PyQkZGBgQMHol+/fhrnk5WVheHDh2PkyJFwcHDIczsjIyMkJSWpxM+ePYu5c+fiyy+/xPv373H79m00a9Ysx74yMzPzNftdXVKpFHp6euKxvr4+UlNTc2134sQJ8e82NjaYOHEiAgIC4OnpiXPnzuHSpUsIDQ39ZPukpCRxCf2ShIVxIiIiIiIql/KxojERlXKpmakYd24cbr25pRSXp9QSi+JRi7vmuvT5kydP4OXlhUuXLgEAxo4di1atWuW6nCERERERUUGwNFCdxVyS7uni4oLDhw+Le3sDgKGhofj7M/Bh3/GHDx/C0NAQAFC/fn3IZDIAQIsWLfDdd9/Bw8MD69evx927d6FQKJCVlaVR7vPmzUNCQgKWLl2qVjtjY2MkJSVBLpdDKv1QWg0PD8eTJ08wfvx4TJ48GQqFAmfPns21MG5mZob4+HjxODMzE7q6uggLC0O9evU+2U7TPcYtLCwQExMjHiclJUFXVzfHHD+V94sXL5Ceno4xY8Zg48aNOS79HhkZqfEs98LEwjgRERERERERlRt3Yu5gyMkhKvG0F4MhT3LAD1/Vx1Bnuxz7EAQBO3bswKRJk8SZIxKJBCNHjkTFihULI20iIiIiIhXqLGleHLp06YJXr15BR0dHjL1+/RoymQx2dnZiLCsrCxkZGQgPD4eJiQkAICYmBqGhoWjatCkAID09HQYGBpDJZEhPT1c7lyNHjmD16tW4ceOGWIRXh4uLCy5evIj27dsD+DBb3M7ODkFBQQCApUuX4uzZs5g1axbMzc2Vit/v37+Hubk5AMDR0RHHjh0Tz0VEREAikcDa2jrH+2u6x7iTkxOuX78Ob29vAEBISAiqV6+eY5vU1FQ4Ozvj5s2b4mzzP/74A3Z2drhx4wYiIiLQv39/pTZmZmY4fvw4XF1dkZycjIiICLVm5RcVFsaJiIiIiIiIqFzY93AfFt5YqBRTyCsgJdwHi3s3xMDmNT7R8m8xMTEYPXo0Dh06JMZsbW2xc+dOtG7dusBzJiIiIiIqrf69j3d4eDhatmyJBg0aoHXr1pg7d2627QRBwNSpU9GuXTuxiJueng5DQ0P06tULvXr1UiuPsLAwDBo0CBs2bIC1tTWSk5OhpaWlVoF8zJgxmDt3rlgYP3PmDDp06CAW+D08PLB582akpqbCzc0NQ4cOxYULF2BnZ4clS5agbdu2AAAvLy/4+vpi6dKl6Nu3L2bNmoVOnTrB2Ng4x/trOvu6T58+cHV1xdSpU2Fraws/Pz8MGjQIAKBQKJCYmAhTU1Ol1bIMDQ1RsWJFTJw4EePGjcPly5fxyy+/4Ny5c2jevDkiIyOV7mFvb4+QkBBxL/hly5bBy8tLo3wLW847wBMREREREZVRkhLwh4iKzv5H+1WK4ulvuiElfDauzWqfp6L4iRMn4ODgoFQUHzZsGO7evcuiOBERERFRDoKDg9GtWzf4+vri6NGjOH/+PL7++mu8fau8R3pMTAz69u2Ly5cv46effsK7d++wceNGHDhwQJwd/eLFCxw+fBi3bt3KcTnvj/z9/ZGamoqhQ4fC2NgYxsbG+OKLL9TKv3Xr1qhSpQrWrVsHuVyOoKAgtGvXTum8RCLBxYsX4eHhgalTp2LIkCFo1KgRJBIJ/Pz8AADW1tY4efIkdu3aBQcHBygUCuzYsUOtXNTRqFEjTJgwAY6OjrC2toYgCBg3bhwA4NmzZzA3N0dCQoJKu+3bt+Px48dwdXXF1q1bsW/fPrRu3Rr6+vqws7NTegGAnZ0d9PX1ERwcjICAAEybNq3Qnik/JEJ28+7LqHTVpfeJiIiIiCif9EvpOlTvUjTbk6wgWVTQLu4UiIrMx5kICQkJ4vKIReVK9BWM/W2sUiz16UhcmTwKVU0N8tTHhg0bMHbs331YWlrC399f7dkqmgpv0xbyN28grVwZdS5eKJJ7EhEREVHJkJ6ejqioKNjb20NfX7+408mzkJAQ3LhxAwEBAQgPD8eqVavQs2dPAB/21vbx8cH69evRvXt3dOvWDQMHDsTly5cxY8YMBAQEwMbGBnK5HB07dkSLFi0we/ZsGBoaIiwsDB07dkSrVq2wfv16cZnywpaUlITOnTvj+PHjsLCwKJJ7FpS7d+8iOjoa7dq1E5dHLwydO3fGsmXL8ryMerZj+69A4Of+SJQJMPVNKtD3kCyMExERERFRvpTWwnh8avEXxs0NWRin8qO4CuNRCVHwOOyhFEuJnIJF3TpiQB5miX/0+vVrNGjQAHFxcejevTs2bdokLhVYFFgYJyIiIiq/SmthfNKkSXj+/Dl69+4NT09P6OrqqlwTGRmJdevWQVtbGz/++GMxZKmerKwsaGvzvfynqPvzKerCeCn9CIuIiIiIiIiIKGd3Y+5i8MnBSrHkx9Px41du6NfMRq2+qlSpgi1btuDt27cYOXKk0h58RERERESkas2aNbleU7NmTSxfvrwIsikYLIrnrKT/fLjHOBERERERERGVKYIgYPGNxSpF8dTnw7D96y65FsXDwsLQvXt3xMXFKcW/+uorfPPNNyyKExERERERlUIsjBMRERERERFRmRIQHoCfH/6sFEt7MQi7BnihzWdWn2ynUCiwevVqNGnSBCdOnMCYMWNQjnagIyIiIiIiKtO4lDoRERERERERlRmvU17jh2s/KMVSn47E/VkToK/z6WX9Xrx4gWHDhuHcuXNiLDQ0FPHx8bCwsCi0fImIiIiIiKhocMY4EREREREREZUJzxKfoWNAR6VY0sMFiPh+co5F8b1798LBwUGpKD558mTcvn2bRXEiIiIiIqIygjPGiYiIiIioXOIWwURlS8ircHx9prdSLP1VL4TO7fbJNu/evcO4cePwyy+/iDFra2ts27YNHTp0KLRciYiIiIiIqOixME5EREREREREpVpCWlo2RfHeGOU4EEZ62X/08dtvv8HLywsvX74UY4MGDcLatWthbm5eqPkSERERERFR0eNS6kREREREVC5JSsAfIsq/rCwFXPc3V4qlv+6O86P+i+mdP/9ku+fPn4tFcXNzc+zbtw979uxhUZyIiIiIqAgpFIriTkEtq1atQmJiYnGnUWL5+voiPT29uNP4JBbGiYiIiIiIiKhUkivkaLSzmVIs430zhP93MWwsDHNsO2zYMPTs2ROdOnXCvXv34OnpWZipEhERERGVS1lZWZgxYwZiY2MBAIsXL4a3tzcAICEhAY0bN8aDBw+U2vj7+0NLSwtSqVR8VapUSaXv3r17Y9OmTXnOJTIyEtevX9e4cLtjxw6cPn0aFSpU0Kh9eSCXyzF27NjiTuOTWBgnIiIiIiIiolInTZ4Gx12OkGhlirGMOFc8mrxN5drMzEwcO3ZMKSaRSLB7926cPn0a1atXL/R8iYiIiIjKIy0tLbx79w7u7u5ITEyEjo4ODAwMAADjx4+HnZ0dPvvsM6U2UqkU/fv3h1wuh1wux8OHD6GjowMAOHbsGFxdXQEAurq6Yjw3kyZNQosWLTBixAjY29sjLCxMreeIjY3FwoULsXfvXmhra4vx2bNnq+Q/bNgwTJkyRTwOCQmBRPL3qnH37t2Dk5MTjI2N0aNHD7x9+1atXNQVEBAAW1tbVKtWDXv37s1TG0EQYGpqColEIr4WLFgAAGjbtq1S/OMLAHx8fPD8+XMEBQUV2vPkBwvjRERERERULkkkxf8iIs0IggDvQG+lmDz5M4ROXqdy7V9//QVXV1d4eHioFMcrVKig9AEVEREREREVnLS0NGRmZmLDhg2wsLDA06dPxXPPnz9HeHg49uzZA0EQkJaWJp7L7nf0jzGpVKpUmM6L8+fPIzAwEJGRkQgLC0PXrl2xZMkStfpYvXo1xo8fr7L10pkzZxAeHq70bDlJSUlBly5d0LFjR9y9excVK1bEqFGj1MpFHaGhoRg8eDBmz56NwMBAfP/993j06FGu7cLDw2Fubo74+HjxNX36dADA8ePHleIbNmxAu3btxLbLly/HDz/8UGjPlB/S4k6AiIiIiIiIiEgdS4OX4l7sPaVY3+rzoKP99/f/BUHA+vXr8e2334ofso0ZMwadOnWCnp5ekeb7T4mnTyNmjR8UKSlqtZPHxBRSRkREREREhWPQoEG4du0ajIyMAABubm6Ii4uDqakpTp06BYlEAkdHR2RmZuLzzz9HYGAggA/LcZ84cQK1a9cG8GEFqMzMDytF/XN2cl4ZGhrC399fzKNRo0b47bff1Orj8OHDOHfunFIsPj4eISEhcHV1xdmzZzFy5Mhc+9m/fz+MjY2xaNEiAMCcOXNQs2ZNpKSk5LhEu6WlJeRyuUr8woULaNy48Sfbbd68GW5ubmJuEyZMwK5du8TZ359y8+ZNODs7w8zMTOXcx58j8GGP+NWrV2PLli1irFGjRnj37h3evn2b7RL4xYmFcSIiIiIiKpc4R5SodAp8EohdYbvEY3lKTaQ9G4X5vg3E2MuXLzFixAjxgzUAqFOnDnbt2lWsRXEAiFnjh4zISI3ba3E/QyIiIiIqJQ4dOiT+PTw8HJ07d4ajoyPq1q2LGjVq4MqVK1i7di1q1Kih1G7AgAHo2LEjVq5cCSMjI3zzzTdqzxL/pxYtWoh/f/v2LbZs2YKpU6fmuX1mZiZkMplKkffcuXOoV68e3N3d81wYv3r1Klq2bCke29jYYPTo0bkWxm/dugVBEFTi1apVy/F+d+7cQZcuXcTj5s2bY/78+bnmefPmTdy8eRNmZmbQ1dXFqFGjMH/+fJUvJRw4cADVqlWDs7OzUtzJyQmhoaFKM8lLAhbGiYiIiIiIiKhUyFJk4duL34rHigwLpD37BnfndhJjv/76K8aMGYN3796JsbFjx2Lp0qU5ftBUVMSZ4lpakFpZqdVWq0IFWE2aVAhZEREREVFpFNWnL+SxsUV6T6mlJewPBOT5+oyMDPj7+2PBggVYu3YtIiMjERERgUmTJiEuLg6NGjXC4sWLMXr0aEgkEmRlZUEQBFSpUgXp6ekwMTFBlSpVAECcNa6pTZs2YeLEiWjbti2GDRuW53axsbGwtLRUiZ85cwbOzs5wdnbGypUroVAooKWV8y7W0dHRaNKkiXgslUqxYcOGXHOwtbXNc77/lJiYCHt7e/HYxMQE0dHRubaLiIhAr169MHnyZDx+/Bj9+vWDg4MDPD09la7z8/PL9ksGVlZWhb53uiZYGCciIiIiIiKiEk+WJUOz3c2UYikR/8GZqW1goq+D9+/fY8KECdizZ494vkqVKti6davSDImSQmplhToXLxR3GkRERERUisljYyF/86a40/ikI0eOYOzYsXBycsK1a9dgb28PPz8/mJubQ19fH0uWLIG7uzs8PT1hbW2N7t274+HDh2jSpAkMDAyQkpICHR0d+Pn5ISMjAwMHDkS/fv00zmfo0KGwsbHBmDFjsHr1akyePDlP7YyMjJCUlKQSP3v2LObOnYsvv/wS79+/x+3bt9GsWbNsevhbZmZmvma/q0sqlSqtmqWvr4/U1NRc2504cUL8u42NDSZOnIiAgAClwvj9+/cREREBDw8PlfZJSUlKS66XFCyMExERERFR+cS11IlKjfMPX2HSte6Q/GPyRcb7ZpjbwwGfVTYGAJWieN++fbFhwwZUrFixqNMlIiIiIioS0mxmMZeke7q4uODw4cNo3ry5GDM0NMSlS5fEYzc3Nzx8+BCGhoYAgPr160MmkwH4sAT6d999Bw8PD6xfvx53796FQqFAVlaWRrnr6enB3d0d8+fPx/Lly/NcGDc2NkZSUhLkcjmk0g+l1fDwcDx58gTjx4/H5MmToVAocPbs2VwL42ZmZoiPjxePMzMzoauri7CwMNSrV++T7TTdY9zCwgIxMTHicVJSEnR1dXPM8VN5v3jxQim2d+9e9O7dO9tCf2RkpMaz3AsTC+NEREREREREVGIN3nwNIfgWWjoZYiwrvQpODFyD2pWMxdiiRYtw/PhxCIKAdevWYfDgwSr73xERERERlSXqLGleHLp06YJXr15BR0dHjL1+/RoymQx2dnZiLCsrCxkZGQgPD4eJiQkAICYmBqGhoWjatCkAID09HQYGBpDJZEhPT1crj+XLl6NKlSoYPHgwgA+zqNWdte3i4oKLFy+iffv2AD7MFrezs0NQUBAAYOnSpTh79ixmzZoFc3NzpeL3+/fvYW5uDgBwdHTEsWPHxHMRERGQSCSwtrbO8f6a7jHu5OSE69evw9vbGwAQEhKC6tWr59gmNTUVzs7OuHnzpjjb/I8//lD6ZwYA+/fvz3YZ+OTkZERERMDBwSHH+xQHFsaJiIiIiIiIqET6evs5hEhmQ0v697KFmQkNcXqQP6oaK3+kUaNGDfz666+oW7cuatSoUdSpEhERERHRvwQHBysdh4eHo2XLlmjQoAFat26NuXPnZttOEARMnToV7dq1E4u46enpMDQ0RK9evdCrVy+18qhTpw5GjhyJatWqoVq1aliyZAkGDBigVh9jxozB3LlzxcL4mTNn0KFDB7FY7OHhgc2bNyM1NRVubm4YOnQoLly4ADs7OyxZsgRt27YFAHh5ecHX1xdLly5F3759MWvWLHTq1AnGxsafuPMHms6+7tOnD1xdXTF16lTY2trCz88PgwYNAgAoFAokJibC1NRU6UvFhoaGqFixIiZOnIhx48bh8uXL+OWXX3Du3DnxmsjISDx9+hQuLi4q91y2bBm8vLw0yrew5bwDPBERERERURklKQF/iOjTlv2xHCGSKUpF8ZomdRE6bgf2bloDBwcHJCYmKrXp2LEji+JERERERCVQcHAwunXrBl9fXxw9ehTnz5/H119/jbdv3ypdFxMTg759++Ly5cv46aef8O7dO2zcuBEHDhwQZ0e/ePEChw8fxq1bt1ChQoVc7+3h4YHp06dj0KBBcHV1RZcuXTBjxgy18m/dujWqVKmCdevWQS6XIygoCO3atVM6L5FIcPHiRXh4eGDq1KkYMmQIGjVqBIlEAj8/PwCAtbU1Tp48iV27dsHBwQEKhQI7duxQKxd1NGrUCBMmTICjoyOsra0hCALGjRsHAHj27BnMzc2RkJCg0m779u14/PgxXF1dsXXrVuzbtw+tW7cWzwcFBcHR0RH6+vpK7YKDgxEQEIBp06YV2jPlh0TIbt59GZWuuvQ+ERERERHlk34pXYcqWVb8b4WM9Fgcp/Lj40yEhIQEcXnE7MiyZGi2W3Vfvtqmn2PxF/PhPdwbv//+OwBg2LBh2LZtW6HlXBjC27SF/M0bSCtXRp2LF4o7HSIiIiIqBdLT0xEVFQV7e3uVQmRJFhISghs3biAgIADh4eFYtWoVevbsCeDD3to+Pj5Yv349unfvjm7dumHgwIG4fPkyZsyYgYCAANjY2EAul6Njx45o0aIFZs+eDUNDQ4SFhaFjx45o1aoV1q9fLy5TXtiSkpLQuXNnHD9+HBYWFkVyz4Jy9+5dREdHo127duLy6IWhc+fOWLZsWZ6XUc92bP8VCPzcH4kyAaa+Sbm+h1RHKf0Ii4iIiIiIKH+49TBRySMIgkpRXMgyQOoLT3StaoJmTZohJSUFAKClpYWqVatCoVBAS4sL4hERERERlTRbt27F8+fPMXToUHh6ekJXV1c8p6OjgyVLlmD06NFYt24d7ty5g8GDB6NNmza4ceOGeJ1UKhX38f7oiy++QHR0dJE9x0fGxsa4fPmy2vuTlwQNGzZEw4YNC/0+J0+eLNE/HxbGiYiIiIiIiKjY3Xh1AyPPjFSKKeSGSAyZgPoR+zDq1EkxXrNmTezcuRMtW7Ys6jSJiIiIiCiP1qxZk+s1NWvWxPLly4sgm4JRkou+JUFJ//mwME5ERERERERExepU1CnMuKS8x58i0wQxJ92QHjQFge/fifFvvvkGy5cvh7GxcVGnSURERERERKUYC+NERERERFQucSV1opLhfux9laK4PMUe0VsUSLq9UIxVqlQJmzdvRo8ePYo6RSIiIiIiIioDWBgnIiIiIiIiomKzNmSt0nFK1EQo0qtDanFMjPXs2RP+/v6wsrIq6vSIiIiIiEoMhUJR3CkQFaiiHtMsjBMRERERUfnEKeNExe7Gqxu4En1FPE7+638Qsj4skR4b+BM8PePQtWtXDBs2DBIJ/6UlIiIiovJJV1cXWlpaePnyJaysrKCrq8vfj6lUEwQBGRkZiImJgZaWFnR1dYvkviyMExEREREREVGR+/Ptnxh5ZiQAIP15OpLuSqBf80NRPHJRV2hpSfDrr78WZ4pERERERCWClpYW7O3t8erVK7x8+bK40yEqMIaGhqhRowa0tLSK5H4sjBMRERERERFRkQp5G4Khp4ZCUAiIC4zDmwNvIMgFVPIMQdjmb6GlxdkvRERERET/pKurixo1akAulyMrK6u40yHKN21tbUil0iJd/YCFcSIiIiIiKpckXEudqFjceHUD065PQ0ZsBl5seoHUR6niucYJV1HR6H/FmB0RERERUcklkUigo6MDHR2d4k6FqFQqmnnpRERERERElC+hoaFwcnKCubk5pk+fDkEQijslKuU0GVMXL15EvXr1YGlpiRUrVmh030nnJyH+Sjwe+zz+R1FcgolTpuHo4UMa9UlERERERESUGxbGiYiIiIiISjiZTIYePXqgadOmCA4ORlhYGLZv317caVEppsmYiomJgYeHBwYOHIhr165hz549CAoKUvve0f7RiN4cDUW6AgCgbVIJFy4EYc3K5dDT09PkcYiIiIiIiIhyxaXUiYiIiIioXCrCLazy7dSpU0hISMCKFStgaGiIRYsWYfz48Rg+fHhxp0allCZjas+ePahatSpmz54NiUSC77//Hlu2bIGbm5ta904KSRL/XtnBBQ8Pr4WZqQkQF6Hx85QqCvnf/1tenpmIiIiIiCivEl8WWtcsjBMRERERERUTmUwGmUymFNPT01OZNXvnzh20aNEChoaGAICGDRsiLCysyPKkskeTMXXnzh20a9cOkv//Vknz5s0xa9asT17/7/GdkJAg/t1US4L/WVdCB7xGVM+++XmUUkeergUIEkgTXiFxmWNxp0NERERERFQiJco+bPdVkFvJlavCuH65etriI5PJsHjxYsyaNYvL4FGZwrFNZRHHNZVVHNuUFyXh/cHcBYsxb948pdicOXMwd+5cpVhiYiLs7e3FY4lEAm1tbcTHx8Pc3LwoUqUyRpMxlZiYiC+++EI8NjExQXR09CfvsXix6vj+KEEhYMazNxpmX4bcK+4EiIiIiIiISra4uDiYmpoWSF8SoSDL7ET48GGJqakpEhISYGJiUtzpEBUYjm0qiziuqazi2KbSIq8zxv/73/8iMzMTK1asEGM2Nja4fv06qlevXiS5UtmiyZjy9PREy5YtMWnSJABAVlYW9PX1kZmZme31/x7f79+/h62tLZ49e1ZgH2oQFbfExETY2Njg+fPn/J2DyhSObSqLOK6prOLYprIqISEBNWrUQHx8PMzMzAqkzxIwR4KIiIiIiKh8yq4Inh0LCwuEhoYqxZKSkqCrq1tYqVEZp8mYsrCwQExMTJ6v/9T4NjU15Qd2VOaYmJhwXFOZxLFNZRHHNZVVHNtUVmlpaRVcXwXWExERERERERUKJycnXL9+XTx+8uQJZDIZLCwsijErKs00GVP/bhMSEsIVC4iIiIiIiKjUYGGciIiIiIiohGvdujUSEhKwc+dOAICvry86dOgAbW3tYs6MSqucxlRiYmK2y6N7eHjgypUrCAoKglwux7Jly9C5c+eiTp2IiIiIiIhII1xKnQqcnp4e5syZk6clIYlKE45tKos4rqms4timskYqlcLf3x+DBg3C9OnTkZWVhYsXLxZ3WlSK5TSmGjZsiFWrVqFnz55KbSwtLbF8+XJ07twZpqamqFChArZs2ZLne/K/zVQWcVxTWcWxTWURxzWVVRzbVFYVxtiWCIIgFFhvREREREREVGiio6MRHBwMFxcXWFlZFXc6VAZoMqYeP36MBw8eoE2bNtzDkIiIiIiIiEoNFsaJiIiIiIiIiIiIiIiIiKhM4x7jRERERERERERERERERERUprEwTkRERESUi8jISFy/fh3p6enFnQoRERERERERERFpgIVxIipT3r9/j759+6JChQpo0qQJgoODlc4/efIEEomkmLJTdu3aNdStW7e406BSorSM7Xnz5sHCwgJ6enrw9PREUlJScadEJVhpGdeTJk1CixYtMGLECNjb2yMsLKy4UyIiIiIiIiIiIiI1sTBejpWWD6MDAgJga2uLatWqYe/evcWdDpVww4cPR0pKCkJCQjBy5Eh4eHggLS2tuNNScfv2bfTq1Qsymay4U6FSojSM7T179mD37t04ffo07t+/jzt37sDX17e406ISrDSM6/PnzyMwMBCRkZEICwtD165dsWTJkuJOi4ioRAoNDYWTkxPMzc0xffp0CIKQa5uLFy+iXr16sLS0xIoVK4ogSyL1aDKu/f39UbVqVejo6KBTp0549epVEWRKpB5NxvZHmZmZcHBwwIULFwovQSIN5GdcDxgwABMnTizE7Ig0p8nYXrp0KSpXrgwTExP06dMHcXFxRZApkXri4uJgb2+PJ0+e5On6gnj/yMJ4OVYaPowODQ3F4MGDMXv2bAQGBuL777/Ho0ePijstKqGioqJw5MgRbN26FXXq1MG4ceOgo6OD8+fPF3dqSlJSUtCrVy+MGzeuuFOhUqK0jO1nz55h+/btaN68OWrXrg1PT0+VL10RfVRaxrWhoSH8/f1hZGQEAGjUqBHi4+OLOSsiopJHJpOhR48eaNq0KYKDgxEWFobt27fn2CYmJgYeHh4YOHAgrl27hj179iAoKKhoEibKA03G9ZUrVzB79mzs2rULUVFRSE9Px7fffls0CRPlkSZj+59+/PFHhIaGFl6CRBrIz7gODAzE+fPnMX/+/MJNkkgDmoztS5cuYceOHbh06RJu376N9PR0/Oc//ymahInyKDY2Ft27d89zUbyg3j+yMF5OlZYPozdv3gw3NzeMHDkSDg4OmDBhAnbt2lXcaVEJdfXqVdSsWRNVq1YVY+PHj4epqWmubS9fvozGjRvD0NAQTk5OSm/wzpw5g3r16sHQ0BAtW7ZERESEeG737t2ws7NDhQoV0KVLlzx9805HRwe///47WrdureYTUnlVWsb2rFmz0LJlS/H40aNHqF27dl4fk8qZ0jKuW7RogTZt2gAA3r59iy1btqB3797qPCoRUblw6tQpJCQkYMWKFahVqxYWLVqELVu25Nhmz549qFq1KmbPno06derg+++/z7UNUVHSZFw/evQI69evR4cOHWBtbY3hw4fzy6JU4mgytj8KDw/HsmXLYGdnV7hJEqlJ03GdlpaGcePGwdfXF2ZmZoWfKJGaNBnbN2/eRNeuXVG3bl3Url0bAwcOxF9//VVEGRPlzYABAzBgwIA8X19Q7x9ZGC+nSsuH0Xfu3EG7du3E4+bNm+P27dt5fUwqZ6Kjo1G5cmWl2IwZM+Dq6ppjO4VCgb59+6Jfv36IjIyEi4sLpk+fLp4fOnQovL298ddff6FBgwbw8fEBACQnJ2P48OHw9fVFWFgYpFIpli1blmueurq6qF69ugZPSOVVaRnb//To0SMcOnQIY8aMUasdlR+lbVxv2rQJNWrUQNWqVTFs2LC8PygRUTlx584dtGjRAoaGhgCAhg0bIiwsLNc27dq1E7fw4vs9Kmk0Gdfe3t5KX6Ljl0WpJNJkbH80evRozJw5E7a2toWZIpHaNB3X8+fPR1paGqRSKc6fP6/W8utERUGTsd2gQQMcPHgQERER4pf8O3bsWBTpEuWZv78/Jk+enOfrC+r9Iwvj5VRp+TA6MTER9vb24rGJiQmio6PVeVQqRzIzM6Gtra1R2zt37mDatGl4+vQpkpKSlL5BZ2BgAJlMBlNTU2zYsAG7d+8GAGhra0NHRwcymQyVKlXC0aNHsXDhwgJ5FqJ/Km1jOysrC8OHDxdX+yDKTmkb10OHDsXhw4fx8OFDrF69WqO8iYjKsn+/d5NIJNDW1s5x+wm+36OSTpNx/U9xcXHYuHEjt9GiEkfTsb1t2zYkJCRwOV4qkTQZ18+ePcOKFStQu3ZtPHv2DNOnT0fv3r1ZHKcSRZOx7e7ujjp16qB27dqoXLkyUlJSMHPmzKJIlyjPatasqdb1BfX+kYXxcqq0fBgtlUqhp6cnHuvr6yM1NVWjvKnsMzMzU/mFwMXFBevXr8+xnZaWFlasWIHq1atj/PjxSEhIQFZWlnh+7969uHDhAqpWrQpXV1fxW0gGBgb49ddf4e/vDysrK7i7uyMyMrLgH4zKvdI2tufNm4eEhAQsXbpUjaek8qa0jWs9PT24u7tj/vz52LZtmxpPSkRUPvz7vRuQ+/s3vt+jkk6Tcf1P48aNg4uLC7p161YY6RFpTJOxHRMTg1mzZmHLli2QSqWFnSKR2jQZ19u3b0flypVx9uxZ+Pj44MKFC7h48SLOnj1b2OkS5ZkmY3v//v14+vQpHj58iLi4ODRo0ABDhgwp7FSJClVBvX9kYbycKi0fRltYWCAmJkY8TkpKgq6urjqPSuWIo6Mj/vrrLyQmJoqxqKgo1KhRI8d2Fy5cwPr16/HgwQMEBwfD29tbPJeSkoKUlBScPXsW7969Q6tWrTBixAgAH779b25ujqtXr+LNmzeoVKkSpk6dWjgPR+VaaRrbR44cwerVq3HgwAFxiSei7JSWcb18+XLs2bNHPJZKpRp/uZCIqCz793s3IPf3b3y/RyWdJuP6o61bt+LSpUvYunVrYaVHpDFNxvaUKVPg7e2Nxo0bF3J2RJrRZFy/ePEC7du3FwstxsbGqFOnDqKiogo1VyJ1aDK29+7di7Fjx6Ju3bqwsLDAqlWrcPDgQbx//76QsyUqPAX1/pGF8XKqtHwY7eTkhOvXr4vHISEh3JuZPsnFxQX169fHqFGjEBkZiQULFiAzMxNt27bNsV1ycjIAICEhAVevXsW0adPEJZMUCgW6deuG3bt3IzY2FlpaWlAoFACA2NhYtG/fHqdPn0ZiYqLSOaKCVFrGdlhYGAYNGoS1a9fC2toaycnJnPVFn1RaxnWdOnUwdepUBAUF4dGjR1iyZAn69euXv4cnIiqD/v3e7cmTJ5DJZLCwsMhzG77fo5JGk3ENADdv3sSUKVOwb98+lW3siEoCTcb2zz//DD8/P5iZmcHMzAxXrlxB9+7d4evrWxQpE+VKk3FtY2ODtLQ08VihUODFixewtbUt1FyJ1KHJ2JbL5Xjz5o14/OrVKwBQmuRIVNoU2PtHgcolhUIhODo6Cp6enkJERIQwf/58oWLFikJycrJ4TVRUlPDvIXLs2DHByMhIePTokXDlyhWhbt26Qo0aNQRBEITExERBT09P2LVrlxAdHS3MmjVL+OKLLwRBEISHDx8K+vr6wqlTp4SXL18KQ4cOFbp27ZprniEhIYKRkZFw//59ITk5WWjSpImwbNmyAvxJUFnz4sULoUuXLoK+vr7QpEkT4ebNm0rnsxvXmZmZwqBBg4QKFSoIDRo0EJYtWyZIpVLh9evXgiAIwq+//ip8/vnngr6+vtCgQQPh4sWLYtt169YJdnZ2gr6+vvDll18KoaGhec41KChIsLW11fxhqVwpDWN78uTJAgClF8c45aQ0jGtBEIQff/xRqFKlimBpaSnMnDlTyMrKyueTExGVPZmZmYKVlZWwY8cOQRAEYfTo0UL37t0FQRCEhIQEISMjQ6VNTEyMoK+vL5w/f17IzMwUunXrJkyYMKFI8ybKiSbj+vXr10KlSpWEBQsWCElJSeKLqCTRZGxHRUUpvb788kth7969Qnx8fFGmTvRJmozrhw8fChUqVBACAgKE58+fCzNmzBAqVqwoJCYmFmnuRDnRZGwvXrxYsLKyEtavXy9s375daNy4seDs7FykeRPlFQAhKipKPC7s948sjJdjpeXD6JkzZwq6urqCmZmZ4OjoKKSmpubzyYmIiIiIiKigHTp0SDAwMBAqVaokVKxYUXzPZ2trKxw6dCjbNuvWrRN0dHQES0tLwdbWVnxvSVRSqDuuV65cqfJlUc5LoZJIk/9m/1ObNm2EoKCgwk2SSE2ajOvjx48LjRs3FvT19YX69esLV65cKcKMifJG3bGdlpYmTJw4UahWrZqgq6srtGnTRnj8+HERZ02UN/8ujBf2+0fJ/9+UqES7e/cuoqOj0a5dO3HPFyIiIiIiIipZoqOjERwcDBcXF1hZWeWpzePHj/HgwQO0adMGJiYmhZwhkfo0GddEpQHHNpVFHNdUVnFsE32Q3/ePLIwTEREREREREREREREREVGZplXcCRARERERERERERERERERERUmFsaJiIiIiIiIiIiIiIiIiKhMY2GciIiIiIiIiIiIiIiIiIjKNBbGiYiIiIiIiIiIiIiIiIioTGNhnIiI8k0mkyErK0spJggCZDKZWv0IggCFQlEgOcnlcly8eFGlv0ePHiElJaVA7kFEREREREREZZtcLse+ffuQkZGhci49PR2CIBTYveLi4pCcnJzvfmJiYgogGyIiorKHhXEiIlJL9erV8fnnn6Nx48awt7fHjBkzMGLECDg4OKB27dowMDBA48aN0ahRIzRq1AgKhQIeHh44duwYAODixYu4cOGC+Hrw4IHY9+rVqzF48GCl+/n4+GDGjBkAgKysLKSnp6vklJmZqVKY37VrF3r06IFr164hLCwMjx8/hlwuR7du3bB161YAHwrxmZmZBfrzISIiIiIiIqKy4/bt25g6dSrmz5+PihUrws7ODvr6+ggODkaTJk3g4OCASpUqYd68eQCAtm3bwt7eHg0aNICJiQlmzpyZ53uNGDECO3bsUIkvWLAAw4YNy1MfYWFhqFatGmJjY/N8XyIiovJCWtwJEBFR6RIdHQ0AePLkCZo3bw4vLy/Ur18fAHDy5EksXboUQUFBSm2GDx+OoUOH4uDBg+jevTsGDhwIAAgPD0fNmjXxww8/ID4+Hnp6etDT01Nqa2hoCG1tbQBAUFAQunXrBiMjI0gkEgAfiuIpKSn47bff0LZtWwBAZGQkZsyYgRo1aqBz586oX78+nJ2dYWtrizdv3mDBggWYNWsWzMzM0LlzZ2zZsqXQfl5EREREREREVHqdPXsWAwcOhImJCebNm4cJEyagbdu20NPTQ1hYGABg7ty5MDIyAgDo6upi06ZN6NChAz7//HP06NEDCxYswLJly2BpaSn2++7dO3h5eWHlypViTE9PD/r6+uLxF198gdOnT8PAwAC6urrZ5vexUP/x/oIgQF9fH61bt4ahoSEAIDU1FampqQgMDETdunUL9gdERERUirAwTkREaklPT8f8+fMRERGBefPmiUVxAHj58iVq166t0qZXr1748ssvUbVqVRgYGGDNmjV49uwZrl+/jitXruDMmTO4cuUKvvzyS7HN27dvkZCQgISEBMhkMvz1119wcnLKdXn269evo3///hg3bhymTZuGatWq4dSpU3j58iU6dOiAa9euQU9PDx07dsTjx48hlfL/ComIiIiIiIgoe/v378eSJUsQGhqa43VaWlpK//v8+XPExsbC2dkZ169fx5AhQ7B27Vrxeh8fH3H7t6ysLGRlZUFLSwtPnjzBb7/9hnr16uHFixewtraGtra22O/HFfM+TiK4cuUKqlSpIn6+4e7ujsmTJ+Prr78WJxUkJyfj6dOn2X5mQ0REVJ5wKXUiIlLbvXv38Ntvv8Hb2xvAh2J0gwYN4OPjg8OHD6NBgwZo0KAB/P398e7dO+zcuRPVqlUT35BFR0ejWbNmYn/a2triG7qPfvvtNyxduhTbtm3D1atXsWTJEqVl1//t4xvDWrVqYeHChZg3bx5MTU0RERGBgwcPonXr1ti0aRMaNGiAOnXqYOvWreKbSiIiIiIiIiKif7t37x7u3r0rzuKePXs2rK2tce3aNURFRaF27dqoX78+fvrpJ6SkpCi1DQwMRPv27aGlpaXymcdHHz+XuH37Nlq1agUAiI+Px7fffovbt28jIyMDlSpVgo+PD3bs2AFLS0t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",
      "text/plain": [
       "<Figure size 2000x1500 with 5 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "{'accuracy': 0.7095854466877324,\n",
       " 'confusion_matrix': array([[162933,   4182,  58249,      0],\n",
       "        [     1,      0,      0,      0],\n",
       "        [  5092,    182,   2570,      0],\n",
       "        [    30,      0,      0,      0]], dtype=int64),\n",
       " 'classification_report': '              precision    recall  f1-score   support\\n\\n           0       0.97      0.72      0.83    225364\\n           1       0.00      0.00      0.00         1\\n           2       0.04      0.33      0.07      7844\\n           3       0.00      0.00      0.00        30\\n\\n    accuracy                           0.71    233239\\n   macro avg       0.25      0.26      0.23    233239\\nweighted avg       0.94      0.71      0.80    233239\\n',\n",
       " 'all_labels': array([0, 0, 0, ..., 0, 0, 0], dtype=int64),\n",
       " 'all_predictions': array([2, 2, 0, ..., 2, 0, 0], dtype=int64),\n",
       " 'all_probabilities': array([[2.6772892e-01, 1.4533590e-01, 5.8693087e-01, 4.3161558e-06],\n",
       "        [3.9352283e-01, 9.7276919e-02, 5.0919443e-01, 5.8818300e-06],\n",
       "        [9.3555260e-01, 6.4447410e-02, 3.1821947e-09, 1.5562649e-09],\n",
       "        ...,\n",
       "        [1.0825248e-01, 2.1655154e-01, 6.7519599e-01, 4.0729886e-10],\n",
       "        [9.3410277e-01, 6.5895364e-02, 1.2232206e-06, 6.3456940e-07],\n",
       "        [9.4204187e-01, 5.7958115e-02, 2.7528181e-11, 3.6718264e-09]],\n",
       "       dtype=float32)}"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lstm_metrics = evaluate_model(\n",
    "    lstm_model, X, y)\n",
    "lstm_metrics"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## GRU"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GRU(\n",
       "  (gru): GRU(20, 64, num_layers=2, batch_first=True, dropout=0.2)\n",
       "  (fc1): Linear(in_features=64, out_features=128, bias=True)\n",
       "  (dropout): Dropout(p=0.3, inplace=False)\n",
       "  (relu): ReLU()\n",
       "  (fc2): Linear(in_features=128, out_features=4, bias=True)\n",
       ")"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from model import GRU\n",
    "gru_model = torch.load('./model/gru_best.pth')\n",
    "gru_model.to(device)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Testing: 100%|██████████| 1823/1823 [00:04<00:00, 382.22it/s, accuracy=96.61%]\n",
      "d:\\Python\\CondaEnvs\\Pytorch\\lib\\site-packages\\sklearn\\metrics\\_classification.py:1565: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n",
      "d:\\Python\\CondaEnvs\\Pytorch\\lib\\site-packages\\sklearn\\metrics\\_classification.py:1565: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n",
      "d:\\Python\\CondaEnvs\\Pytorch\\lib\\site-packages\\sklearn\\metrics\\_classification.py:1565: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Confusion Matrix:\n",
      "[[225342      0     80      0]\n",
      " [     2      0      0      0]\n",
      " [  7786      0      0      0]\n",
      " [    30      0      0      0]]\n",
      "\n",
      "Classification Report:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.97      1.00      0.98    225422\n",
      "           1       0.00      0.00      0.00         2\n",
      "           2       0.00      0.00      0.00      7786\n",
      "           3       0.00      0.00      0.00        30\n",
      "\n",
      "    accuracy                           0.97    233240\n",
      "   macro avg       0.24      0.25      0.25    233240\n",
      "weighted avg       0.93      0.97      0.95    233240\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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VFRISomXLltndNi4uTlFRUerRo4cJSQEAQHJhGIYsFou8vLyss/79+yt16tTq3Lmz/Pz8TEyHeIuhciN+tpkd+riW05bicZY4lVhQwmbm7eGt39r9ZlIiALDn0K3UfX195evrq/Pnz2vs2LEKDw/X999/rzZt2lj/kY2NjZXFYnHUwwIAAAAA4PKio6O1fv1669nhHh4eWrdunQzDsLttXFyc4uLiKMYBAHBjN2/e1FtvvaUiRYpo+PDh1rmXl5d69uxpYjJIksViKN+gNTaz0U2LKiiFj0mJnk90fLRKLyxtNz/Q9oAJaQDg8RxWjP/1j+vp06d17949TZ06VRs3btSECRM0Y8YMzZgxQ3Xq1HHUwwEAAAAA4DaCg4O1fPlySX9+4LxSpUoqUKCAXnzxRdWuXVulSpUyNyAAAEg2Vq5cqc6dO+vmzZtauXKl6tWrp3LlypkdC/+l+titNsdBKXzUIiSHSWmez9n7Z9VoRSObWZXsVTSp+iSTEgHA4zl8T46goCBVrVpVHh4eqlmzpmrWrKlVq1ZpypQpqlWrls11z5JaTBxnqsP1+Hqb9/8pAAAAZ8ZPUXBWXl5emjRpks6cOaNDhw7pzJkzFOMAAEARERHq27evZs6caZ2lS5dO4eHhJqbC/5q1/ZzO3X5oPa7xYkbNbB9iYqJnFx4TbleKF0lfhFIcQLLl8GI8Q4YMatWqlc2sfv36ql+/vqMfCgAAAAAAt+Pp6anSpUurdOnSeuONN8yOAwAAkoEdO3aoXbt2Onv2rHXWsGFDzZgxQxkzZjQxGf7bmVsPNHzVcZvZjHb2W5A7g7tRd1VlSRWbWa1ctTSm6hiTEgHAv3NoMb5w4UJFRUWpaNGiKlOmjHX+6NEjlS5dWseOHZMkbdy4UalTp1bZsmUd+fAAAAAA8MQ8PDzMjgA8tffee08pUqT4x93YDMNQTEyMunfvrty5cyddOAAAkORiYmL0ySefaNSoUbJY/twxNVWqVBo3bpw6duzIz7zJyLStZzRi7Umb2aGPajnln9Hmi5vVe3Nvm9m7pd5Vx5c7mpQIAJ6MQ3cP/PDDD/XVV19p//79NnMfHx9duXJFkmSxWNS7d29t3rzZkQ8NAAAAAIDLmzt3rjw8PGQYhgzD0KRJk6zfG4ah6dOnS5J8fX3l7e3wTeIAAEAycuXKFZUtW1YjRoywluKvvPKKDh06pE6dOjll4eqqOs3da1eKzwkNUVCAj0mJno1hGGq/tr1dKV43T11KcQBOweGvkg8cOGD/IN7e8vX1lSTNnj1bXl5e6t+/v6MfGgAAAAAAlxYcHKzhw4dbjxcuXGhzvHz5cptjAADgutKnT28txH18fDRs2DD1799fXl5eJifDfxu17qR+PnnTZra6d0UVzhpkUqJns/3KdnXf2N1u/nqB1zW0wlATEgHA03NoMf5vn0CLiorSxx9/rK+//pp/nAEAAACYypMTaOCE/vd1978dAwAA1+Xn56eFCxcqNDRUM2fOVPHixc2OhP8SERWrV8ds1a2IaJv575/WkZ+3c/UjvTf11uZL9rsAb2i2QZlTZjYhEQA8G4efMR4eHq7atWsrT548yp49u3LkyKEcOXJIko4fP65ixYqpatWqjn5YAAAAAAAAAABckmEYWrhwoYoXL64iRYpY50WKFNHevXv5cFwyk9D1xCXp2NDaTlWKx1niVGJBCbt5yYwlNa/uPBMSAcDzcUgxfuvWLc2ePVs3b95UeHi46tWrJw8PD0VGRurQoUNau3atbt++rXHjxmnJkiWOeEgAAAAAeC6cMQ5ndOvWLfXu/fc1He/cuWNzfPXqVetxx44dOXMMAAAXcOfOHXXr1k1Lly5VsWLFtGfPHvn5+VnXKcWTl96LftOPh67azX8dVF0p/Rx+rmKiOR92Xg2WN7Cbr2myRjkCc5iQCACen0P+Fn7//fe1Y8cOeXh4KHv27BoyZIi+/vpr5cmTRxUqVJD053XQUqZMqfr162vTpk1spQ4AAAAAwFPq16+fUqRIYX1NPWzYMJv1Dz/8UIZhKCYmxuYNcwAA4JzWrl2rjh076vr165KkQ4cOafny5XrjjTdMToaEDPz+iF0pPqV1SdUtksWkRM8m3hKfYCn+a+tflcI7hQmJAMAxHFKMjx07VkFBQcqbN68k6dSpU3r77bf1448//v1A3t6aMmWK6tWrp1GjRmnQoEGOeGgAAAAAANzCoEGD1KBBA5UvX946MwxD33zzjbZv365ixYqpW7duJiYEAACO8vDhQ/Xr109Tp061zoKDgzVt2jQ1a9bMxGR4nNHrTmrRrxdtZs54PXFJ6r25t81xw3wN9VnFz0xKAwCO45BiPE2aNJL+3rKlU6dOGjRokCpVqqRWrVqpdu3a1tuOHDlS1apV07vvvqsUKfhkEQAAAABzsOUknM3kyZP1ww8/6M6dO6pevboqVaqkefPmKVOmTKpatao++eQTFS1a1LpzGwAAcE579uxR27ZtderUKeusTp06mj17trJkca4zj91FdFy8vtpyxmZ29vPX5Olk12+Kt8Sr39Z+2nZ5m3XWrGAzfVz+YxNTAYDjJMoFLcaPH6+iRYtq0qRJ2rp1q7744gsZhiFJKlKkiHLmzKlVq1apefPmifHwAAAAAAC4nJw5c+rIkSO6fPmy1q9fr7Vr16pTp0566623JEmXL1/W2bNnKcYBAHBSsbGx+vTTT/XZZ58pPj5ekhQQEKAxY8aoa9eufLAzmboW9kjlR2yymZ0cXsfpSnFJarm6pU7ePWk9zpYqG6U4AJfi0GLcMAz17dtXZcqUUbFixfTNN99o9uzZyp49uywWi/V2jRs31vfff08xDgAAAADAE3r48KE2b94swzCUJ08e9ejRQ5K0adOfb8TWqlVLvr6+1uNXX33VtKwAAODpHT16VJ9++qn1vfQyZcpowYIFKliwoMnJ8DirDl/V29/8ZjPrUTWf/H2cb/v0Lj91sSnFJWlO7TkmpQGAxOHQYrxly5YKDw9XVFSUPD09tXPnTklSdHS0oqOjrberUKGCypYt68iHBgAAAICn4oQncMDN5cqVSyNHjpSnp+c/3i4uLk5xcXEU4wAAOJkSJUpo0KBBGjFihD766CMNGjRI3t6JsukrHOBmeJRdKf5W5bwaUOcFkxI9m3hLvOr9UE9XHlyxmR9qd0ieHv/8cycAOBsP4689zhORYRg6ceKELBaLXn755cR+uMcKj7L8+40AJ+PrzQ8nAADAXP5O+l5d/1W/mx1BX9QvZHYEuBCLxaLjx4+b+rr7n4SHhysoKEhhYWEKDAyULu6W1g2UIm+bHQ1wW5FXDd3YFa/4qMR9HEusrI+Rsayn0pXgvYxkLTpCenTvz+8bTZZKtDE3jwu7du2aMmbMKC+vv88ujo2N1bFjx1S8eHHzguFfTdt6RiPW2p5dvax7BZXKldakRM/m29+/1fDdw+3mB9oekI+njwmJAOBvdq8hHcBhb2HFxcVp1apVaty4sd2ah4eHChQooPTp0yssLEy7du1SiRIl5O/v76iHBwAAAICnwiUa4Wz+6XW3JMXHx+uVV15xntfd27+Urh4wOwXg1m7/GqyoW0n794RH3F3pfmSSPiaeg29KsxO4rCVLlqh79+4aMGCAPvjgA+vcx8eHUjyZW3f0ul0p/lH9l5yqFL/96LaqfVstwbUdrXZQigNwWQ4rxg3D0MiRIx/7At3Hx0c+Pn/+ZdqtWzf16dNHoaGhjnp4AAAAAABcmsu97o5+8Pf3KTNI4tMqQFKzWP4+S9XL30j0/xv6pzUU+EIqyT9V4j4QHCNbKalAbbNTuJx79+6pZ8+eWrRokSTpo48+Up06dSjDncDNiCi1nfmrfr8RYTNf/FY5lcubzqRUT2/d+XXqv7W/3bxo+qJa8NoCtk8H4NIcVoz/9QJ8zZo16tatW4KfSvf09NQvv/yi8PBwtW3b1lEPDQAAAACAy3Pp193vHpO8/cxOAbifA29KN/+8Pm6BA8fl4UkZAiSmjRs3qkOHDrpy5e9rOTdt2lQ5c+Y0MRWexMxfzurT1Sfs5pv7VVWe9M6zs8If9/6wK8UDfQO1odkGBfgEmJQKAJKOw3/ajYqKUsOGDWWxWNSyZUtVqVJFhmFowYIFMgxD3377rQYMGCBvbye9ECEAAAAAl+Dp4WH6F/AseN0NAIBzefTokd555x3VrFnTWoqnSZNG33zzjRYtWqTg4GCTE+JxHkTH6f2lhxMsxTf2reJUpfh3f3ynpj82tZmNqTJGO1rtoBQH4DYS5VVyjhw5FBAQoPz588vLy0spUqRQ2bJlJUlFixZV8+bNE+NhAQAAAABwC7zuBgDAOezfv19t2rTRyZN/X5O6evXqmjt3rrJnz25iMvwTwzD046GremfxQbu1RV3KqXw+59k63TAMffDLB1pzbo3NfFqNaaqQrYJJqQDAHA4pxg3D0IgRI/TgwQNdvnz5H2/bpUsXRzwkAAAAADwXNouFM+F1NwAAzmf9+vWqX7++4uLiJEn+/v4aNWqU3n77bXly6YJkKyo2XvUm/KIztx7azENyp9VXrUspQ2rnufzL7Ue3Ve3banbzL6p8QSkOwC055F/f2NhYHTp0SCdPntTw4cMd8UsCAAAAAID/x+tuAACcT6VKlZQvXz5JUsmSJXXgwAH17t2bUjyZC/lso10p/n6dF/RdtwpOU4rHWmI1Zt+YBEvxHa12qE7uOiakAgDzOeRfYF9fXy1ZskSlS5fW1KlT//G2//nPf3T+/HlHPCwAAAAAAG6B190AADifgIAALVy4UEOGDNGuXbv04osvmh0J/2LUupOKiIqzHtcrkkVHh9ZW96r5TEz1dA7ePKgyC8to7rG5NvOsKbNqX5t9CvQNNCcYACQDDv9omoeHh+7cuaOYmBjdvHlTd+/eVUxMjM6ePStJioiI0H/+8x9HPywAAAAAPBUPD/O/gGfB624AAJKf69evq3nz5jp16pTNvHTp0ho+fLh8fX1NSoY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      "text/plain": [
       "<Figure size 2000x1500 with 5 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "{'accuracy': 0.9661378837249185,\n",
       " 'confusion_matrix': array([[225342,      0,     80,      0],\n",
       "        [     2,      0,      0,      0],\n",
       "        [  7786,      0,      0,      0],\n",
       "        [    30,      0,      0,      0]], dtype=int64),\n",
       " 'classification_report': '              precision    recall  f1-score   support\\n\\n           0       0.97      1.00      0.98    225422\\n           1       0.00      0.00      0.00         2\\n           2       0.00      0.00      0.00      7786\\n           3       0.00      0.00      0.00        30\\n\\n    accuracy                           0.97    233240\\n   macro avg       0.24      0.25      0.25    233240\\nweighted avg       0.93      0.97      0.95    233240\\n',\n",
       " 'all_labels': array([0, 0, 2, ..., 0, 0, 0], dtype=int64),\n",
       " 'all_predictions': array([0, 0, 0, ..., 0, 0, 0], dtype=int64),\n",
       " 'all_probabilities': array([[9.9803835e-01, 3.5455677e-04, 1.6065318e-03, 6.3622934e-07],\n",
       "        [9.9802929e-01, 7.8691496e-04, 1.1827800e-03, 1.0747802e-06],\n",
       "        [9.9863273e-01, 9.4697985e-04, 4.1851183e-04, 1.7506217e-06],\n",
       "        ...,\n",
       "        [9.9721682e-01, 7.8278751e-04, 1.9945896e-03, 5.7219399e-06],\n",
       "        [9.9375111e-01, 4.6439390e-04, 5.7829958e-03, 1.4686841e-06],\n",
       "        [9.9680030e-01, 2.7969021e-03, 4.0025430e-04, 2.5076838e-06]],\n",
       "       dtype=float32)}"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gru_metrics = evaluate_model(\n",
    "    gru_model, X, y)\n",
    "gru_metrics"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Transformer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Transformer(\n",
       "  (embedding): Linear(in_features=20, out_features=64, bias=True)\n",
       "  (pos_encoder): TransformerPositionalEncoding()\n",
       "  (transformer_encoder): TransformerEncoder(\n",
       "    (layers): ModuleList(\n",
       "      (0-1): 2 x TransformerEncoderLayer(\n",
       "        (self_attn): MultiheadAttention(\n",
       "          (out_proj): NonDynamicallyQuantizableLinear(in_features=64, out_features=64, bias=True)\n",
       "        )\n",
       "        (linear1): Linear(in_features=64, out_features=128, bias=True)\n",
       "        (dropout): Dropout(p=0.1, inplace=False)\n",
       "        (linear2): Linear(in_features=128, out_features=64, bias=True)\n",
       "        (norm1): LayerNorm((64,), eps=1e-05, elementwise_affine=True)\n",
       "        (norm2): LayerNorm((64,), eps=1e-05, elementwise_affine=True)\n",
       "        (dropout1): Dropout(p=0.1, inplace=False)\n",
       "        (dropout2): Dropout(p=0.1, inplace=False)\n",
       "      )\n",
       "    )\n",
       "  )\n",
       "  (fc): Linear(in_features=64, out_features=4, bias=True)\n",
       "  (dropout): Dropout(p=0.1, inplace=False)\n",
       ")"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from model import Transformer\n",
    "model = torch.load('./model/transformer_best.pth')\n",
    "model.to(device)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Testing:   0%|          | 0/1823 [00:00<?, ?it/s]d:\\Python\\CondaEnvs\\Pytorch\\lib\\site-packages\\torch\\nn\\modules\\transformer.py:720: UserWarning: 1Torch was not compiled with flash attention. (Triggered internally at C:\\cb\\pytorch_1000000000000\\work\\aten\\src\\ATen\\native\\transformers\\cuda\\sdp_utils.cpp:455.)\n",
      "  return torch._transformer_encoder_layer_fwd(\n",
      "Testing: 100%|██████████| 1823/1823 [00:06<00:00, 274.11it/s, accuracy=24.11%]\n",
      "d:\\Python\\CondaEnvs\\Pytorch\\lib\\site-packages\\sklearn\\metrics\\_classification.py:1565: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n",
      "d:\\Python\\CondaEnvs\\Pytorch\\lib\\site-packages\\sklearn\\metrics\\_classification.py:1565: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n",
      "d:\\Python\\CondaEnvs\\Pytorch\\lib\\site-packages\\sklearn\\metrics\\_classification.py:1565: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Confusion Matrix:\n",
      "[[ 50102      0 175320      0]\n",
      " [     1      0      1      0]\n",
      " [  1647      0   6139      0]\n",
      " [    22      0      8      0]]\n",
      "\n",
      "Classification Report:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.97      0.22      0.36    225422\n",
      "           1       0.00      0.00      0.00         2\n",
      "           2       0.03      0.79      0.06      7786\n",
      "           3       0.00      0.00      0.00        30\n",
      "\n",
      "    accuracy                           0.24    233240\n",
      "   macro avg       0.25      0.25      0.11    233240\n",
      "weighted avg       0.94      0.24      0.35    233240\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2000x1500 with 5 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "{'accuracy': 0.2411293088664037,\n",
       " 'confusion_matrix': array([[ 50102,      0, 175320,      0],\n",
       "        [     1,      0,      1,      0],\n",
       "        [  1647,      0,   6139,      0],\n",
       "        [    22,      0,      8,      0]], dtype=int64),\n",
       " 'classification_report': '              precision    recall  f1-score   support\\n\\n           0       0.97      0.22      0.36    225422\\n           1       0.00      0.00      0.00         2\\n           2       0.03      0.79      0.06      7786\\n           3       0.00      0.00      0.00        30\\n\\n    accuracy                           0.24    233240\\n   macro avg       0.25      0.25      0.11    233240\\nweighted avg       0.94      0.24      0.35    233240\\n',\n",
       " 'all_labels': array([0, 0, 2, ..., 0, 0, 0], dtype=int64),\n",
       " 'all_predictions': array([2, 2, 2, ..., 2, 0, 2], dtype=int64),\n",
       " 'all_probabilities': array([[1.0301970e-03, 5.1193754e-03, 9.9383003e-01, 2.0425259e-05],\n",
       "        [1.1138283e-03, 6.5811146e-03, 9.9227166e-01, 3.3429511e-05],\n",
       "        [3.4565546e-04, 3.8833506e-03, 9.9574137e-01, 2.9604003e-05],\n",
       "        ...,\n",
       "        [6.6654668e-03, 9.9366507e-04, 9.9233425e-01, 6.6571315e-06],\n",
       "        [9.9350280e-01, 6.5466616e-04, 5.8323471e-03, 1.0167618e-05],\n",
       "        [7.5137540e-04, 5.6162211e-03, 9.9355561e-01, 7.6825381e-05]],\n",
       "       dtype=float32)}"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "transformer_metrics = evaluate_model(\n",
    "    model, X, y)\n",
    "transformer_metrics"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## ARIMA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ARIMA(\n",
       "  (lstm): LSTM(20, 64, num_layers=2, batch_first=True, dropout=0.2)\n",
       "  (attention): MultiheadAttention(\n",
       "    (out_proj): NonDynamicallyQuantizableLinear(in_features=64, out_features=64, bias=True)\n",
       "  )\n",
       "  (fc1): Linear(in_features=64, out_features=32, bias=True)\n",
       "  (relu): ReLU()\n",
       "  (dropout): Dropout(p=0.2, inplace=False)\n",
       "  (fc2): Linear(in_features=32, out_features=4, bias=True)\n",
       ")"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from model import ARIMA\n",
    "arima_model = torch.load('./model/arima_best.pth')\n",
    "arima_model.to(device)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Testing: 100%|██████████| 1823/1823 [00:06<00:00, 294.42it/s, accuracy=96.53%]\n",
      "d:\\Python\\CondaEnvs\\Pytorch\\lib\\site-packages\\sklearn\\metrics\\_classification.py:1565: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n",
      "d:\\Python\\CondaEnvs\\Pytorch\\lib\\site-packages\\sklearn\\metrics\\_classification.py:1565: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n",
      "d:\\Python\\CondaEnvs\\Pytorch\\lib\\site-packages\\sklearn\\metrics\\_classification.py:1565: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Confusion Matrix:\n",
      "[[225130      0    292      0]\n",
      " [     2      0      0      0]\n",
      " [  7772      0     14      0]\n",
      " [    30      0      0      0]]\n",
      "\n",
      "Classification Report:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.97      1.00      0.98    225422\n",
      "           1       0.00      0.00      0.00         2\n",
      "           2       0.05      0.00      0.00      7786\n",
      "           3       0.00      0.00      0.00        30\n",
      "\n",
      "    accuracy                           0.97    233240\n",
      "   macro avg       0.25      0.25      0.25    233240\n",
      "weighted avg       0.94      0.97      0.95    233240\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2000x1500 with 5 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "{'accuracy': 0.9652889727319499,\n",
       " 'confusion_matrix': array([[225130,      0,    292,      0],\n",
       "        [     2,      0,      0,      0],\n",
       "        [  7772,      0,     14,      0],\n",
       "        [    30,      0,      0,      0]], dtype=int64),\n",
       " 'classification_report': '              precision    recall  f1-score   support\\n\\n           0       0.97      1.00      0.98    225422\\n           1       0.00      0.00      0.00         2\\n           2       0.05      0.00      0.00      7786\\n           3       0.00      0.00      0.00        30\\n\\n    accuracy                           0.97    233240\\n   macro avg       0.25      0.25      0.25    233240\\nweighted avg       0.94      0.97      0.95    233240\\n',\n",
       " 'all_labels': array([0, 0, 2, ..., 0, 0, 0], dtype=int64),\n",
       " 'all_predictions': array([0, 0, 0, ..., 0, 0, 0], dtype=int64),\n",
       " 'all_probabilities': array([[9.32403088e-01, 6.62453398e-02, 1.29174534e-03, 5.96978680e-05],\n",
       "        [9.39037204e-01, 5.89054301e-02, 2.01951084e-03, 3.77966462e-05],\n",
       "        [9.21816349e-01, 7.78999254e-02, 2.27026772e-04, 5.66945928e-05],\n",
       "        ...,\n",
       "        [9.98677552e-01, 3.84668645e-04, 9.37749865e-04, 1.83320850e-12],\n",
       "        [9.92605686e-01, 2.57099164e-03, 4.82330658e-03, 1.39557055e-11],\n",
       "        [9.19986725e-01, 7.98805207e-02, 8.40226348e-05, 4.88056321e-05]],\n",
       "       dtype=float32)}"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arima_metrics = evaluate_model(arima_model, X, y)\n",
    "arima_metrics"
   ]
  }
 ],
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